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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
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"source": [
"### **A Comprehensive Guide for Logistic Regression - CIS 490: AI for Data Analytics for Cybersecurity**\n",
"*By Raul Y. Reyes and Dr. Roberto Mejias*\n",
"\n",
"\n",
"---\n",
"**Logistic regression** is a supervised learning algorithm used for classification tasks. Unlike linear regression, which predicts a continuous output, logistic regression predicts the probability that a given input belongs to a particular class. It is commonly used when the target variable is binary (e.g., 0 or 1, yes or no).\n",
"\n",
"---\n",
"\n",
"### Key Idea of Logistic Regression\n",
"\n",
"Logistic regression models the relationship between the input features $x_1, x_2, \\ldots, x_n$ and the **log-odds** of the probability $p$ of belonging to the positive class:\n",
"\n",
"$\\log\\left(\\frac{p}{1-p}\\right) = \\beta_0 + \\beta_1 x_1 + \\beta_2 x_2 + \\ldots + \\beta_n x_n$\n",
"\n",
"Here:\n",
"- $p$: Probability of the positive class.\n",
"- $\\frac{p}{1-p}$: Odds of the positive class.\n",
"- $\\beta_0, \\beta_1, \\ldots, \\beta_n$: Parameters (weights) of the model.\n",
"\n",
"To convert the log-odds back into probabilities, we use the **sigmoid function**:\n",
"\n",
"$p = \\frac{1}{1 + e^{-(\\beta_0 + \\beta_1 x_1 + \\ldots + \\beta_n x_n)}}$\n",
"\n",
"The output $p$ is a value between 0 and 1, representing the probability of the positive class.\n",
"\n",
"---\n",
"\n",
"### How Logistic Regression Works\n",
"\n",
"1. **Model Initialization**:\n",
" - Start with randomly initialized coefficients $\\beta_0, \\beta_1, \\ldots, \\beta_n$.\n",
" - The model predicts probabilities using the sigmoid function.\n",
"\n",
"2. **Loss Function**:\n",
" - Logistic regression uses the **log-loss (cross-entropy loss)** to measure how well the model predicts probabilities:\n",
" $L = -\\frac{1}{N} \\sum_{i=1}^N \\left[ y_i \\log(p_i) + (1-y_i) \\log(1-p_i) \\right]$\n",
" Where:\n",
" - $y_i$: Actual class label (0 or 1) for observation $i$.\n",
" - $p_i$: Predicted probability for observation $i$.\n",
"\n",
"3. **Optimization**:\n",
" - The algorithm adjusts the coefficients $\\beta_0, \\beta_1, \\ldots, \\beta_n$ to minimize the loss function using methods like **gradient descent**.\n",
"\n",
"4. **Prediction**:\n",
" - The model outputs a probability $p$ for each input.\n",
" - A threshold (e.g., 0.5) is applied to classify:\n",
" $\\text{Class} =\n",
" \\begin{cases}\n",
" 1 & \\text{if } p \\geq 0.5 \\\\\n",
" 0 & \\text{if } p < 0.5\n",
" \\end{cases}$\n",
"\n",
"---\n",
"\n",
"### Example: Logistic Regression for Intrusion Detection\n",
"\n",
"#### Problem:\n",
"Predict whether a network connection is **malicious** ($y = 1$) or **normal** ($y = 0$) based on network traffic features ($x$).\n",
"\n",
"1. **Dataset**:\n",
" - $x$: Features like connection duration, bytes transferred, error rates, protocol type, etc.\n",
" - $y$: [0, 0, 1, 1, 0] (normal or attack).\n",
"\n",
"2. **Logistic Regression Model**:\n",
" - The model fits the sigmoid function:\n",
" $p = \\frac{1}{1 + e^{-(\\beta_0 + \\beta_1 x_1 + \\ldots + \\beta_n x_n)}}$\n",
"\n",
"3. **Prediction**:\n",
" - For a connection with high SYN error rate and many connections to the same host:\n",
" $p \\approx 0.92$\n",
" - If $p \\geq 0.5$, predict \"attack\" ($y = 1$).\n",
"\n",
"---\n",
"\n",
"### Strengths of Logistic Regression\n",
"\n",
"1. **Simplicity**: Easy to implement and interpret.\n",
"2. **Probabilistic Output**: Provides class probabilities, useful for threat scoring.\n",
"3. **Efficiency**: Computationally efficient, even on large network datasets.\n",
"\n",
"---\n",
"\n",
"### Weaknesses of Logistic Regression\n",
"\n",
"1. **Linear Decision Boundary**: Assumes a linear relationship between features and log-odds, which may not capture complex attack patterns.\n",
"2. **Feature Engineering Required**: Sensitive to irrelevant or highly correlated features.\n",
"3. **Imbalanced Data**: Performance can be poor if normal traffic vastly outnumbers attacks without proper handling.\n",
"\n",
"---\n",
"\n",
"### Applications of Logistic Regression in Cybersecurity\n",
"\n",
"1. **Network Intrusion Detection**: Classifying network traffic as malicious or benign.\n",
"2. **Phishing Detection**: Identifying phishing URLs or emails.\n",
"3. **Malware Classification**: Detecting malicious software based on behavioral features.\n",
"4. **Fraud Detection**: Identifying fraudulent transactions or account activity.\n",
"5. **Spam Filtering**: Classifying messages as spam or legitimate.\n",
"\n",
"---\n",
"\n",
"Logistic regression is a robust and interpretable algorithm for binary classification tasks, offering both probabilistic predictions and straightforward decision boundaries — making it an excellent starting point for cybersecurity analytics."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3hhAPEqfDiUg"
},
"source": [
"### Why Logistic Regression is a Perfect Fit for Network Intrusion Detection\n",
"\n",
"---\n",
"\n",
"### **1. The Nature of the Problem**\n",
"The goal of this analysis is to predict whether a network connection is **normal or an attack**, which is a binary classification problem. Logistic regression is designed specifically for binary outcomes.\n",
"\n",
"- **Why Binary Classification?**\n",
" Logistic regression models the probability of a binary outcome:\n",
"\n",
" $P(Y=1|X) = \\frac{1}{1 + e^{-(\\beta_0 + \\beta_1X_1 + \\beta_2X_2 + \\dots + \\beta_nX_n)}}$\n",
"\n",
" The output is a probability value between 0 and 1. Based on a threshold (e.g., 0.5), the model predicts the class:\n",
" - **1 (Attack)** if $P(Y=1|X) \\geq 0.5$\n",
" - **0 (Normal)** if $P(Y=1|X) < 0.5$\n",
"\n",
" This aligns perfectly with our target variable, which indicates whether a network connection is normal (`0`) or an attack (`1`).\n",
"\n",
"---\n",
"\n",
"### **2. Features of the Dataset**\n",
"The KDD Cup 99 dataset contains both numerical and categorical variables. Logistic regression can naturally handle such features with preprocessing:\n",
"1. **Numerical Features:** Variables like `duration`, `src_bytes`, `dst_bytes`, `count`, and `serror_rate` are continuous and describe quantitative aspects of network connections.\n",
"2. **Categorical Features:** Variables like `protocol_type` (TCP/UDP/ICMP), `service` (HTTP/FTP/SMTP), and `flag` (SF/S0/REJ) provide qualitative information about the connection type.\n",
"\n",
"- **Why Logistic Regression Handles These Well?**\n",
" - With proper preprocessing (one-hot encoding for categorical data, scaling for numerical data), logistic regression can effectively model the relationship between these features and the target variable.\n",
"\n",
"---\n",
"\n",
"### **3. Probabilistic Nature**\n",
"Logistic regression outputs probabilities, which is advantageous for intrusion detection:\n",
"- **Threat Scoring:** A probability of 0.95 means the model is very confident the connection is malicious — this can trigger an immediate alert.\n",
"- **Threshold Flexibility:** Security teams can adjust the detection threshold:\n",
" - A **lower threshold** (e.g., 0.3) catches more attacks but may generate more false alarms.\n",
" - A **higher threshold** (e.g., 0.7) reduces false alarms but may miss some attacks.\n",
"\n",
"---\n",
"\n",
"### **4. Interpretability**\n",
"Logistic regression provides clear, interpretable results — critical for cybersecurity:\n",
"1. **Feature Importance:** Each feature has a coefficient ($\\beta$) quantifying its impact on the probability of an attack.\n",
" - A large positive coefficient for `serror_rate` means high SYN error rates strongly indicate an attack.\n",
" - A negative coefficient for `dst_bytes` might mean connections with high response data are less likely to be attacks.\n",
"2. **Explainability:** Security analysts need to understand **why** an alert was triggered. Logistic regression provides transparent reasoning.\n",
"3. **Regulatory Compliance:** Organizations must often justify their detection methods — logistic regression's transparency helps.\n",
"\n",
"---\n",
"\n",
"### **5. The KDD Cup 99 Dataset**\n",
"We will use the **KDD Cup 1999 dataset**, one of the most well-known benchmark datasets in network intrusion detection research:\n",
"- Created for the Third International Knowledge Discovery and Data Mining Tools Competition\n",
"- Contains ~494,000 network connection records (we'll use a 10% subset)\n",
"- Each record has 41 features describing a network connection\n",
"- Labels indicate normal traffic or one of four attack categories:\n",
" - **DoS (Denial of Service):** Flooding attacks like SYN flood, smurf\n",
" - **R2L (Remote to Local):** Unauthorized access from a remote machine\n",
" - **U2R (User to Root):** Privilege escalation attacks\n",
" - **Probe:** Surveillance/scanning attacks like port scanning\n",
"\n",
"For this lesson, we simplify the problem to **binary classification**: normal (0) vs. attack (1).\n",
"\n",
"---\n",
"\n",
"### **6. Comparisons to Other Models**\n",
"While more complex models exist (e.g., random forests, neural networks), logistic regression stands out for learning:\n",
"1. **Interpretable Results:** Unlike black-box models, logistic regression shows exactly how each feature contributes.\n",
"2. **Simplicity and Speed:** Fast to train, ideal for real-time detection on resource-constrained systems.\n",
"3. **Baseline Model:** Often used as a baseline before trying more complex approaches — understanding it is foundational.\n",
"\n",
"---\n",
"\n",
"### **Conclusion**\n",
"Logistic regression is an excellent choice for this lesson because:\n",
"1. It aligns with the binary classification nature of intrusion detection.\n",
"2. It provides interpretable results critical for security operations.\n",
"3. It handles the mix of numerical and categorical network features effectively.\n",
"4. It introduces fundamental ML concepts applicable to more advanced cybersecurity analytics.\n",
"\n",
"### **A Picture Is Worth a Thousand Words**\n",
"\n",
"![CIS490_Logistic_Regression](https://drive.google.com/uc?export=view&id=1tiyOfzx1L2-1APlWKCg7hnwJJYMLkkU-)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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},
"outputs": [],
"source": [
"# =============================================================================\n",
"# PHASE 1: IMPORT LIBRARIES\n",
"# =============================================================================\n",
"# These are the core libraries we need for data analysis and machine learning.\n",
"# pandas: Data manipulation and analysis (DataFrames, reading CSV files, etc.)\n",
"# numpy: Numerical computing (arrays, mathematical operations)\n",
"# matplotlib & seaborn: Data visualization (plots, charts, heatmaps)\n",
"# warnings: Suppress non-critical warning messages for cleaner output\n",
"# =============================================================================\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zMK73P1cDiUh"
},
"source": [
"## Phase 1: Data Loading and Initial Exploration\n",
"\n",
"We will use the **KDD Cup 1999** dataset, a classic benchmark dataset for network intrusion detection.\n",
"This dataset is publicly available through scikit-learn's built-in datasets module, so no manual download is needed.\n",
"\n",
"The dataset contains network connection records with 41 features and a label indicating whether the\n",
"connection is normal or a specific type of attack."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yUlD31-xDiUh",
"outputId": "e6e85f1d-f743-4f1a-c68a-0380f11a9bf4"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading KDD Cup 99 dataset... (this may take a moment on first run)\n",
"Dataset loaded successfully!\n",
"Total records: 494,021\n",
"Total features: 41\n"
]
}
],
"source": [
"# =============================================================================\n",
"# LOAD THE KDD CUP 99 DATASET\n",
"# =============================================================================\n",
"# sklearn.datasets.fetch_kddcup99() downloads the KDD Cup 1999 dataset directly.\n",
"# - percent10=True: Downloads only 10% of the full dataset (~494,000 records)\n",
"# This keeps the data manageable while still being representative.\n",
"# - as_frame=True: Returns data as a pandas DataFrame (easier to work with)\n",
"#\n",
"# The dataset is hosted by scikit-learn and is downloaded automatically on first run.\n",
"# Subsequent runs use a cached version.\n",
"#\n",
"# IMPORTANT: The sklearn loader returns all columns as 'object' dtype, even\n",
"# numerical ones. We must explicitly convert numerical columns to proper\n",
"# numeric types (int/float) for mathematical operations to work correctly.\n",
"# =============================================================================\n",
"\n",
"from sklearn.datasets import fetch_kddcup99\n",
"\n",
"# Fetch the dataset (10% subset for manageable size)\n",
"print(\"Downloading KDD Cup 99 dataset... (this may take a moment on first run)\")\n",
"kdd = fetch_kddcup99(subset=None, percent10=True, as_frame=True)\n",
"\n",
"# The dataset comes with a 'data' DataFrame and a 'target' Series\n",
"# Let's combine them into a single DataFrame for easier manipulation\n",
"network_data = kdd['data'].copy()\n",
"network_data['label'] = kdd['target']\n",
"\n",
"# CRITICAL FIX: Convert numerical columns from 'object' dtype to proper numeric types.\n",
"# The sklearn KDD loader stores ALL columns as 'object' dtype, but many columns\n",
"# contain integers and floats. We need to convert them for math operations,\n",
"# scaling, and model training to work correctly.\n",
"categorical_cols = ['protocol_type', 'service', 'flag']\n",
"for col in network_data.columns:\n",
" if col not in categorical_cols and col != 'label':\n",
" network_data[col] = pd.to_numeric(network_data[col], errors='coerce')\n",
"\n",
"print(f\"Dataset loaded successfully!\")\n",
"print(f\"Total records: {network_data.shape[0]:,}\")\n",
"print(f\"Total features: {network_data.shape[1] - 1}\") # -1 for the label column"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dqJ9g3jRDiUh",
"outputId": "35d2120b-e76b-475b-dd1a-b77b05bbd200"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Target variable distribution:\n",
"is_attack\n",
"1 396743\n",
"0 97278\n",
"Name: count, dtype: int64\n",
"\n",
"Attack percentage: 80.3%\n",
"Normal percentage: 19.7%\n",
"\n",
"Original attack type distribution (top 10):\n",
"label\n",
"b'smurf.' 280790\n",
"b'neptune.' 107201\n",
"b'normal.' 97278\n",
"b'back.' 2203\n",
"b'satan.' 1589\n",
"b'ipsweep.' 1247\n",
"b'portsweep.' 1040\n",
"b'warezclient.' 1020\n",
"b'teardrop.' 979\n",
"b'pod.' 264\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"# =============================================================================\n",
"# CREATE BINARY TARGET VARIABLE AND SELECT KEY FEATURES\n",
"# =============================================================================\n",
"# The original dataset has multiple attack types (smurf, neptune, back, etc.)\n",
"# For logistic regression (binary classification), we simplify this:\n",
"# - 0 = Normal traffic (label is b'normal.')\n",
"# - 1 = Attack (any label that is NOT b'normal.')\n",
"#\n",
"# We also select a curated subset of features that are:\n",
"# 1. Easy to understand (\"EASY\"), data isn't easy...\n",
"# 2. A good mix of numerical and categorical features\n",
"# 3. Known to be informative for intrusion detection\n",
"#\n",
"# FEATURE DESCRIPTIONS:\n",
"# Numerical:\n",
"# - duration: Length of the connection in seconds\n",
"# - src_bytes: Bytes sent from source to destination\n",
"# - dst_bytes: Bytes sent from destination to source\n",
"# - count: Number of connections to the same host in the past 2 seconds\n",
"# - srv_count: Number of connections to the same service in the past 2 seconds\n",
"# - serror_rate: Percentage of connections with SYN errors (indicator of SYN flood)\n",
"# - same_srv_rate: Percentage of connections to the same service\n",
"# - dst_host_count: Count of connections to the same destination host\n",
"# - dst_host_srv_count: Count of connections to the same service on destination host\n",
"# - dst_host_same_srv_rate: % of connections to same service on destination host\n",
"#\n",
"# Categorical:\n",
"# - protocol_type: Network protocol (TCP, UDP, ICMP)\n",
"# - service: Network service on destination (HTTP, FTP, SMTP, etc.)\n",
"# - flag: Status of the connection (SF=normal, S0=no response, REJ=rejected, etc.)\n",
"# =============================================================================\n",
"\n",
"# Create binary target: 0 = normal, 1 = attack\n",
"network_data['is_attack'] = (network_data['label'] != b'normal.').astype(int)\n",
"\n",
"# Let's see the distribution of attack vs normal traffic\n",
"print(\"Target variable distribution:\")\n",
"print(network_data['is_attack'].value_counts())\n",
"print(f\"\\nAttack percentage: {network_data['is_attack'].mean()*100:.1f}%\")\n",
"print(f\"Normal percentage: {(1-network_data['is_attack'].mean())*100:.1f}%\")\n",
"\n",
"# Show the original attack type distribution (for context)\n",
"print(\"\\nOriginal attack type distribution (top 10):\")\n",
"print(network_data['label'].value_counts().head(10))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1CNT_oF6DiUh",
"outputId": "db19d24d-8ff1-485a-b7a3-2ebabd67bba4"
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"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Working dataset shape: (45000, 14)\n",
"\n",
"Target distribution after sampling:\n",
"is_attack\n",
"1 36139\n",
"0 8861\n",
"Name: count, dtype: int64\n",
"\n",
"Attack ratio preserved: 80.3%\n",
"\n",
"Top services kept: ['ecr_i', 'private', 'http', 'smtp', 'other', 'domain_u', 'ftp_data', 'eco_i']\n"
]
}
],
"source": [
"# =============================================================================\n",
"# SELECT FEATURES AND SAMPLE THE DATASET\n",
"# =============================================================================\n",
"# We select meaningful features and sample down to ~45,000 records\n",
"# to keep the dataset manageable (similar size to the loan dataset example).\n",
"#\n",
"# Why sample? The full 10% KDD dataset has ~494,000 records. While logistic\n",
"# regression can handle this, a smaller sample makes the lesson run faster\n",
"# and keeps visualizations readable.\n",
"#\n",
"# We use stratified sampling to maintain the same ratio of attacks to normal\n",
"# traffic in our sample as in the full dataset.\n",
"# =============================================================================\n",
"\n",
"# Select our curated features\n",
"selected_features = [\n",
" # Numerical features - describe the connection's behavior\n",
" 'duration', # How long the connection lasted\n",
" 'src_bytes', # Data sent by the source (client)\n",
" 'dst_bytes', # Data sent by the destination (server)\n",
" 'count', # Recent connections to same host\n",
" 'srv_count', # Recent connections to same service\n",
" 'serror_rate', # SYN error rate (key attack indicator!)\n",
" 'same_srv_rate', # Rate of connections to same service\n",
" 'dst_host_count', # Connections to destination host\n",
" 'dst_host_srv_count', # Connections to same service on dest host\n",
" 'dst_host_same_srv_rate', # % same service on dest host\n",
"\n",
" # Categorical features - describe the connection's type\n",
" 'protocol_type', # TCP, UDP, or ICMP\n",
" 'service', # Network service (http, ftp, smtp, etc.)\n",
" 'flag', # Connection status flag\n",
"]\n",
"\n",
"# Build our working dataset with selected features + target\n",
"intrusion_data = network_data[selected_features + ['is_attack']].copy()\n",
"\n",
"# Decode byte strings to regular strings for categorical columns\n",
"# (sklearn loads KDD data with byte-encoded strings like b'tcp' instead of 'tcp')\n",
"for col in ['protocol_type', 'service', 'flag']:\n",
" intrusion_data[col] = intrusion_data[col].apply(\n",
" lambda x: x.decode() if isinstance(x, bytes) else str(x)\n",
" )\n",
"\n",
"# Reduce cardinality of 'service' column\n",
"# The service column has 66+ unique values, which creates too many dummy variables.\n",
"# We keep the top 8 most common services and group the rest as 'other'.\n",
"# This is a practical preprocessing decision - too many dummy variables can\n",
"# cause overfitting and make the model harder to interpret.\n",
"top_services = intrusion_data['service'].value_counts().head(8).index.tolist()\n",
"intrusion_data['service'] = intrusion_data['service'].apply(\n",
" lambda x: x if x in top_services else 'other'\n",
")\n",
"\n",
"# Sample down to ~45,000 records using stratified sampling\n",
"from sklearn.model_selection import train_test_split\n",
"intrusion_data, _ = train_test_split(\n",
" intrusion_data,\n",
" train_size=45000,\n",
" random_state=42,\n",
" stratify=intrusion_data['is_attack']\n",
")\n",
"\n",
"# Reset index after sampling\n",
"intrusion_data = intrusion_data.reset_index(drop=True)\n",
"\n",
"print(f\"Working dataset shape: {intrusion_data.shape}\")\n",
"print(f\"\\nTarget distribution after sampling:\")\n",
"print(intrusion_data['is_attack'].value_counts())\n",
"print(f\"\\nAttack ratio preserved: {intrusion_data['is_attack'].mean()*100:.1f}%\")\n",
"print(f\"\\nTop services kept: {top_services}\")"
]
},
{
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"height": 444
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{
"output_type": "display_data",
"data": {
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" duration src_bytes dst_bytes count srv_count serror_rate \\\n",
"0 0 1032 0 511 511 0.0 \n",
"1 0 0 0 24 19 1.0 \n",
"2 0 0 0 114 12 1.0 \n",
"3 0 0 0 1 2 0.0 \n",
"4 0 208 6208 26 45 0.0 \n",
"... ... ... ... ... ... ... \n",
"44995 0 520 0 509 509 0.0 \n",
"44996 0 1032 0 511 511 0.0 \n",
"44997 0 0 0 188 1 1.0 \n",
"44998 0 0 0 209 9 1.0 \n",
"44999 9 105 145 2 2 0.0 \n",
"\n",
" same_srv_rate dst_host_count dst_host_srv_count \\\n",
"0 1.00 255 255 \n",
"1 0.79 255 19 \n",
"2 0.11 255 9 \n",
"3 1.00 5 19 \n",
"4 1.00 81 255 \n",
"... ... ... ... \n",
"44995 1.00 255 255 \n",
"44996 1.00 255 255 \n",
"44997 0.01 255 1 \n",
"44998 0.04 255 18 \n",
"44999 1.00 255 244 \n",
"\n",
" dst_host_same_srv_rate protocol_type service flag is_attack \n",
"0 1.00 icmp ecr_i SF 1 \n",
"1 0.07 tcp private S0 1 \n",
"2 0.04 tcp private S0 1 \n",
"3 1.00 tcp http REJ 0 \n",
"4 1.00 tcp http SF 0 \n",
"... ... ... ... ... ... \n",
"44995 1.00 icmp ecr_i SF 1 \n",
"44996 1.00 icmp ecr_i SF 1 \n",
"44997 0.00 tcp private S0 1 \n",
"44998 0.07 tcp private S0 1 \n",
"44999 0.96 udp private SF 0 \n",
"\n",
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"metadata": {}
}
],
"source": [
"# =============================================================================\n",
"# DISPLAY THE DATASET\n",
"# =============================================================================\n",
"# display() renders the DataFrame as a nicely formatted HTML table in Colab.\n",
"# This gives us a quick visual overview of what our data looks like.\n",
"# Select our curated features\n",
" # # Numerical features - describe the connection's behavior\n",
" # 'duration', # How long the connection lasted\n",
" # 'src_bytes', # Data sent by the source (client)\n",
" # 'dst_bytes', # Data sent by the destination (server)\n",
" # 'count', # Recent connections to same host\n",
" # 'srv_count', # Recent connections to same service\n",
" # 'serror_rate', # SYN error rate (key attack indicator!)\n",
" # 'same_srv_rate', # Rate of connections to same service\n",
" # 'dst_host_count', # Connections to destination host\n",
" # 'dst_host_srv_count', # Connections to same service on dest host\n",
" # 'dst_host_same_srv_rate', # % same service on dest host\n",
"\n",
" # # Categorical features - describe the connection's type\n",
" # 'protocol_type', # TCP, UDP, or ICMP\n",
" # 'service', # Network service (http, ftp, smtp, etc.)\n",
" # 'flag', # Connection status flag\n",
"# =============================================================================\n",
"\n",
"display(intrusion_data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 320
},
"id": "q-qUo-DqDiUh",
"outputId": "3e014f93-587a-4f67-ff61-e95b0cf4a3d7"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
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" duration src_bytes dst_bytes count srv_count \\\n",
"count 45000.000000 4.500000e+04 4.500000e+04 45000.000000 45000.000000 \n",
"mean 47.068044 2.022491e+03 8.578359e+02 331.868689 292.864467 \n",
"std 702.713455 6.894985e+04 2.821375e+04 213.382341 246.284441 \n",
"min 0.000000 0.000000e+00 0.000000e+00 1.000000 1.000000 \n",
"25% 0.000000 4.500000e+01 0.000000e+00 116.000000 10.000000 \n",
"50% 0.000000 5.200000e+02 0.000000e+00 510.000000 510.000000 \n",
"75% 0.000000 1.032000e+03 0.000000e+00 511.000000 511.000000 \n",
"max 40929.000000 5.135678e+06 5.151385e+06 511.000000 511.000000 \n",
"\n",
" serror_rate same_srv_rate dst_host_count dst_host_srv_count \\\n",
"count 45000.000000 45000.000000 45000.000000 45000.000000 \n",
"mean 0.174984 0.793032 232.438489 188.900333 \n",
"std 0.379376 0.387032 64.884651 105.877099 \n",
"min 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 1.000000 255.000000 50.000000 \n",
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"mean 0.754602 0.803089 \n",
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"50% 1.000000 1.000000 \n",
"75% 1.000000 1.000000 \n",
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" <td>4.500000e+04</td>\n",
" <td>45000.000000</td>\n",
" <td>45000.000000</td>\n",
" <td>45000.000000</td>\n",
" <td>45000.000000</td>\n",
" <td>45000.000000</td>\n",
" <td>45000.000000</td>\n",
" <td>45000.000000</td>\n",
" <td>45000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>47.068044</td>\n",
" <td>2.022491e+03</td>\n",
" <td>8.578359e+02</td>\n",
" <td>331.868689</td>\n",
" <td>292.864467</td>\n",
" <td>0.174984</td>\n",
" <td>0.793032</td>\n",
" <td>232.438489</td>\n",
" <td>188.900333</td>\n",
" <td>0.754602</td>\n",
" <td>0.803089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>702.713455</td>\n",
" <td>6.894985e+04</td>\n",
" <td>2.821375e+04</td>\n",
" <td>213.382341</td>\n",
" <td>246.284441</td>\n",
" <td>0.379376</td>\n",
" <td>0.387032</td>\n",
" <td>64.884651</td>\n",
" <td>105.877099</td>\n",
" <td>0.410405</td>\n",
" <td>0.397669</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>4.500000e+01</td>\n",
" <td>0.000000e+00</td>\n",
" <td>116.000000</td>\n",
" <td>10.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>255.000000</td>\n",
" <td>50.000000</td>\n",
" <td>0.430000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.000000</td>\n",
" <td>5.200000e+02</td>\n",
" <td>0.000000e+00</td>\n",
" <td>510.000000</td>\n",
" <td>510.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>255.000000</td>\n",
" <td>255.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.000000</td>\n",
" <td>1.032000e+03</td>\n",
" <td>0.000000e+00</td>\n",
" <td>511.000000</td>\n",
" <td>511.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>255.000000</td>\n",
" <td>255.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>40929.000000</td>\n",
" <td>5.135678e+06</td>\n",
" <td>5.151385e+06</td>\n",
" <td>511.000000</td>\n",
" <td>511.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>255.000000</td>\n",
" <td>255.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"summary": "{\n \"name\": \"intrusion_data\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"duration\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 19862.123564043766,\n \"min\": 0.0,\n \"max\": 45000.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 47.068044444444446,\n 40929.0,\n 702.7134554289782\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"src_bytes\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1809988.0455286675,\n \"min\": 0.0,\n \"max\": 5135678.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 2022.4909333333333,\n 520.0,\n 45000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"dst_bytes\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1817628.541479665,\n \"min\": 0.0,\n \"max\": 5151385.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 857.8358888888889,\n 5151385.0,\n 28213.7498478484\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 15800.26808797263,\n \"min\": 1.0,\n \"max\": 45000.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 45000.0,\n 331.86868888888887,\n 510.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"srv_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 15806.137033268886,\n \"min\": 1.0,\n \"max\": 45000.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 45000.0,\n 292.86446666666666,\n 510.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"serror_rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 15909.824073390575,\n \"min\": 0.0,\n \"max\": 45000.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.1749842222222222,\n 1.0,\n 0.37937567762210916\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"same_srv_rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 15909.640948177585,\n \"min\": 0.0,\n \"max\": 45000.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.7930324444444444,\n 1.0,\n 0.38703200517697434\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"dst_host_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 15843.685244177901,\n \"min\": 0.0,\n \"max\": 45000.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 232.43848888888888,\n 255.0,\n 64.88465077098235\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"dst_host_srv_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 15854.15908227967,\n \"min\": 0.0,\n \"max\": 45000.0,\n \"num_unique_values\": 6,\n \"samples\": [\n 45000.0,\n 188.90033333333332,\n 255.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"dst_host_same_srv_rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 15909.67049786572,\n \"min\": 0.0,\n \"max\": 45000.0,\n \"num_unique_values\": 6,\n \"samples\": [\n 45000.0,\n 0.7546022222222224,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"is_attack\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 15909.639902972442,\n \"min\": 0.0,\n \"max\": 45000.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.8030888888888889,\n 1.0,\n 0.3976690076790781\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 6
}
],
"source": [
"# =============================================================================\n",
"# STATISTICAL SUMMARY OF THE DATASET\n",
"# =============================================================================\n",
"# .describe() computes summary statistics for all numerical columns:\n",
"# - count: Number of non-null values\n",
"# - mean: Average value\n",
"# - std: Standard deviation (spread of the data)\n",
"# - min/25%/50%/75%/max: Distribution percentiles\n",
"#\n",
"# For cybersecurity, notice:\n",
"# - src_bytes and dst_bytes may have extreme outliers (large data transfers)\n",
"# - serror_rate ranges from 0 to 1 (0% to 100% SYN errors)\n",
"# - count and srv_count show connection frequency patterns\n",
"# =============================================================================\n",
"\n",
"intrusion_data.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Tvhva2RNDiUi",
"outputId": "c8ee0fda-f704-4780-c3ff-e7cc4f486bfb"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Missing values per column:\n",
"duration 0\n",
"src_bytes 0\n",
"dst_bytes 0\n",
"count 0\n",
"srv_count 0\n",
"serror_rate 0\n",
"same_srv_rate 0\n",
"dst_host_count 0\n",
"dst_host_srv_count 0\n",
"dst_host_same_srv_rate 0\n",
"protocol_type 0\n",
"service 0\n",
"flag 0\n",
"is_attack 0\n",
"dtype: int64\n",
"\n",
"Total missing values: 0\n"
]
}
],
"source": [
"# =============================================================================\n",
"# CHECK FOR MISSING VALUES\n",
"# =============================================================================\n",
"# Before building a model, we must check for missing data.\n",
"# Logistic regression CANNOT handle missing values (NaN).\n",
"# If missing values exist, we would need to either:\n",
"# - Fill them (imputation) with mean, median, or mode\n",
"# - Drop rows/columns with too many missing values\n",
"#\n",
"# .isna().sum() counts the number of NaN values in each column.\n",
"# =============================================================================\n",
"\n",
"print(\"Missing values per column:\")\n",
"print(intrusion_data.isna().sum())\n",
"print(f\"\\nTotal missing values: {intrusion_data.isna().sum().sum()}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ys5ZbILODiUi"
},
"source": [
"## Phase 2: Data Preprocessing\n",
"\n",
"Before we can train a logistic regression model, we need to prepare our data.\n",
"This involves three key steps:\n",
"1. **One-Hot Encoding**: Convert categorical variables (protocol_type, service, flag) into numerical format\n",
"2. **Boolean Conversion**: Ensure all binary columns are represented as 0/1 integers\n",
"3. **Feature Scaling**: Standardize numerical features so they're on the same scale"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "STgyz12EDiUi",
"outputId": "2ba1bdfc-d698-4d94-b3c5-0fee570a3c6d"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"BEFORE encoding - Original categorical columns:\n",
" protocol_type unique values: ['icmp' 'tcp' 'udp']\n",
" service unique values: ['ecr_i' 'private' 'http' 'other' 'smtp' 'ftp_data' 'domain_u' 'eco_i']\n",
" flag unique values: ['SF' 'S0' 'REJ' 'RSTR' 'S1' 'RSTO' 'S3' 'SH' 'S2']\n",
"\n",
"Original shape: (45000, 14)\n",
"\n",
"AFTER encoding - New shape: (45000, 28)\n",
"\n",
"New columns created:\n",
" protocol_type_tcp\n",
" protocol_type_udp\n",
" service_eco_i\n",
" service_ecr_i\n",
" service_ftp_data\n",
" service_http\n",
" service_other\n",
" service_private\n",
" service_smtp\n",
" flag_RSTO\n",
" flag_RSTR\n",
" flag_S0\n",
" flag_S1\n",
" flag_S2\n",
" flag_S3\n",
" flag_SF\n",
" flag_SH\n"
]
}
],
"source": [
"# =============================================================================\n",
"# PREVIEW: ONE-HOT ENCODING (before applying to our working data)\n",
"# =============================================================================\n",
"# One-hot encoding converts categorical variables into binary (0/1) columns.\n",
"# For example, protocol_type with values [tcp, udp, icmp] becomes:\n",
"# - protocol_type_tcp: 1 if tcp, 0 otherwise\n",
"# - protocol_type_udp: 1 if udp, 0 otherwise\n",
"# (icmp is the \"dropped first\" reference category)\n",
"#\n",
"# pd.get_dummies() performs one-hot encoding automatically.\n",
"# drop_first=True drops one category per variable to avoid multicollinearity\n",
"# (the \"dummy variable trap\" — if we know it's NOT tcp and NOT udp, it MUST be icmp).\n",
"#\n",
"# Let's preview what this looks like before applying it permanently.\n",
"# =============================================================================\n",
"\n",
"# Preview the encoding (using a copy so we don't modify the original yet)\n",
"intrusion_data_dummies = pd.get_dummies(intrusion_data, drop_first=True)\n",
"\n",
"print(\"BEFORE encoding - Original categorical columns:\")\n",
"print(f\" protocol_type unique values: {intrusion_data['protocol_type'].unique()}\")\n",
"print(f\" service unique values: {intrusion_data['service'].unique()}\")\n",
"print(f\" flag unique values: {intrusion_data['flag'].unique()}\")\n",
"print(f\"\\nOriginal shape: {intrusion_data.shape}\")\n",
"\n",
"print(f\"\\nAFTER encoding - New shape: {intrusion_data_dummies.shape}\")\n",
"print(f\"\\nNew columns created:\")\n",
"for col in intrusion_data_dummies.columns:\n",
" if col not in intrusion_data.columns:\n",
" print(f\" {col}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QoVc5Z64DiUi"
},
"source": [
"### **Why One-Hot Encoding is Suitable for Network Features**\n",
"\n",
"1. **Interpretability:**\n",
" - Each category (e.g., each protocol type, each service) gets its own binary column.\n",
" - For example, `protocol_type_tcp` = 1 means the connection uses TCP; = 0 means it does not.\n",
"\n",
"2. **Compatibility with Logistic Regression:**\n",
" - Logistic regression assumes linear relationships between features and log-odds.\n",
" - One-hot encoding avoids introducing false ordinal relationships (e.g., TCP is not \"greater\" than UDP).\n",
"\n",
"3. **Non-ordinal Nature of Network Variables:**\n",
" - `protocol_type`: TCP, UDP, and ICMP are categories with no inherent order.\n",
" - `service`: HTTP, FTP, SMTP are different services — not ranked.\n",
" - `flag`: SF, S0, REJ are different connection states — not ordered.\n",
"\n",
"---\n",
"\n",
"### **Alternative Encoding Methods**\n",
"\n",
"| Method | Description | Drawback for This Use Case |\n",
"|--------|-------------|---------------------------|\n",
"| **Label Encoding** | Assigns numbers (tcp=1, udp=2, icmp=3) | Implies false ordering (udp > tcp?) |\n",
"| **Target Encoding** | Replace category with mean of target | Risk of data leakage |\n",
"| **Frequency Encoding** | Replace with count frequency | Loses categorical meaning |\n",
"\n",
"**One-hot encoding is the best choice here** because it preserves the categorical nature of network features without introducing false relationships."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 325
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"id": "Q6o2fmTZDiUi",
"outputId": "48373e96-bf62-458a-956b-446da7a76c3f"
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"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Categorical variables encoded successfully!\n",
"New dataset shape: (45000, 28)\n",
"\n",
"All columns after encoding:\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" duration src_bytes dst_bytes count srv_count serror_rate \\\n",
"0 0 1032 0 511 511 0.0 \n",
"1 0 0 0 24 19 1.0 \n",
"2 0 0 0 114 12 1.0 \n",
"3 0 0 0 1 2 0.0 \n",
"4 0 208 6208 26 45 0.0 \n",
"\n",
" same_srv_rate dst_host_count dst_host_srv_count dst_host_same_srv_rate \\\n",
"0 1.00 255 255 1.00 \n",
"1 0.79 255 19 0.07 \n",
"2 0.11 255 9 0.04 \n",
"3 1.00 5 19 1.00 \n",
"4 1.00 81 255 1.00 \n",
"\n",
" ... service_private service_smtp flag_RSTO flag_RSTR flag_S0 flag_S1 \\\n",
"0 ... False False False False False False \n",
"1 ... True False False False True False \n",
"2 ... True False False False True False \n",
"3 ... False False False False False False \n",
"4 ... False False False False False False \n",
"\n",
" flag_S2 flag_S3 flag_SF flag_SH \n",
"0 False False True False \n",
"1 False False False False \n",
"2 False False False False \n",
"3 False False False False \n",
"4 False False True False \n",
"\n",
"[5 rows x 28 columns]"
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"source": [
"# =============================================================================\n",
"# APPLY ONE-HOT ENCODING TO THE WORKING DATASET\n",
"# =============================================================================\n",
"# Now we apply the encoding permanently to our intrusion_data DataFrame.\n",
"# After this step, all categorical columns (protocol_type, service, flag)\n",
"# will be replaced with binary (0/1) indicator columns.\n",
"# =============================================================================\n",
"\n",
"intrusion_data = pd.get_dummies(intrusion_data, drop_first=True)\n",
"\n",
"print(\"Categorical variables encoded successfully!\")\n",
"print(f\"New dataset shape: {intrusion_data.shape}\")\n",
"print(f\"\\nAll columns after encoding:\")\n",
"display(intrusion_data.head())"
]
},
{
"cell_type": "code",
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"colab": {
"base_uri": "https://localhost:8080/",
"height": 360
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"id": "bUb6DqqzDiUi",
"outputId": "af87972b-8b19-44d4-f22f-3c25a645d8c3"
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{
"output_type": "stream",
"name": "stdout",
"text": [
"Converted 17 boolean columns to integers (1/0).\n",
"\n",
"Data types after conversion:\n",
"int64 25\n",
"float64 3\n",
"Name: count, dtype: int64\n"
]
},
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"text/plain": [
" duration src_bytes dst_bytes count srv_count serror_rate \\\n",
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" + ' to learn more about interactive tables.';\n",
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"source": [
"# =============================================================================\n",
"# CONVERT BOOLEAN COLUMNS TO INTEGERS (True/False -> 1/0)\n",
"# =============================================================================\n",
"# pd.get_dummies() sometimes creates boolean (True/False) columns instead of\n",
"# integer (1/0) columns. While Python treats True as 1 and False as 0\n",
"# internally, we convert them explicitly for:\n",
"# 1. Consistency - all features should be numeric integers\n",
"# 2. Clarity - 1/0 is more intuitive than True/False in ML contexts\n",
"# 3. Compatibility - some ML operations expect numeric types explicitly\n",
"# =============================================================================\n",
"\n",
"boolean_cols = intrusion_data.select_dtypes(include=['bool']).columns\n",
"intrusion_data[boolean_cols] = intrusion_data[boolean_cols].astype(int)\n",
"\n",
"print(f\"Converted {len(boolean_cols)} boolean columns to integers (1/0).\")\n",
"print(f\"\\nData types after conversion:\")\n",
"print(intrusion_data.dtypes.value_counts())\n",
"display(intrusion_data.head())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "G8GQlfOQDiUi"
},
"source": [
"### **Why Convert `True`/`False` to `1`/`0`?**\n",
"\n",
"1. **Compatibility with Machine Learning Models:**\n",
" - Most ML models expect numerical inputs. While many libraries handle `True`/`False` as `1`/`0` internally, explicit conversion avoids unexpected behavior.\n",
"\n",
"2. **Interpretability:**\n",
" - When reviewing model coefficients or data statistics, `1`/`0` values are clearer than `True`/`False`.\n",
"\n",
"3. **Consistency:**\n",
" - All binary features should use the same representation to avoid confusion during analysis."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kfVdQTLHDiUi",
"outputId": "ac7f98f3-c9fb-414d-ea64-c10e7e68fa66"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Scaled numerical columns. Sample data after scaling:\n",
" duration src_bytes dst_bytes count srv_count serror_rate \\\n",
"0 -0.066981 -0.014366 -0.030405 0.839494 0.885716 -0.461248 \n",
"1 -0.066981 -0.029333 -0.030405 -1.442819 -1.111997 2.174691 \n",
"2 -0.066981 -0.029333 -0.030405 -1.021036 -1.140420 2.174691 \n",
"3 -0.066981 -0.029333 -0.030405 -1.550608 -1.181023 -0.461248 \n",
"4 -0.066981 -0.026316 0.189632 -1.433446 -1.006427 -0.461248 \n",
"\n",
" same_srv_rate dst_host_count dst_host_srv_count dst_host_same_srv_rate \n",
"0 0.534762 0.347721 0.624313 0.597947 \n",
"1 -0.007835 0.347721 -1.604712 -1.668133 \n",
"2 -1.764815 0.347721 -1.699162 -1.741233 \n",
"3 0.534762 -3.505313 -1.604712 0.597947 \n",
"4 0.534762 -2.333991 0.624313 0.597947 \n",
"\n",
"Verification - Mean of scaled columns (should be ~0):\n",
"duration 0.0\n",
"src_bytes 0.0\n",
"dst_bytes 0.0\n",
"count 0.0\n",
"srv_count 0.0\n",
"serror_rate 0.0\n",
"same_srv_rate 0.0\n",
"dst_host_count 0.0\n",
"dst_host_srv_count 0.0\n",
"dst_host_same_srv_rate -0.0\n",
"dtype: float64\n"
]
}
],
"source": [
"# =============================================================================\n",
"# FEATURE SCALING (STANDARDIZATION)\n",
"# =============================================================================\n",
"# StandardScaler transforms numerical features to have:\n",
"# - Mean = 0\n",
"# - Standard deviation = 1\n",
"#\n",
"# WHY IS THIS NECESSARY?\n",
"# Logistic regression uses gradient-based optimization to find the best\n",
"# coefficients. Features with very different scales (e.g., src_bytes in\n",
"# millions vs. serror_rate between 0-1) can cause:\n",
"# 1. Slow convergence - the optimizer takes many more steps\n",
"# 2. Numerical instability - very large values can cause overflow\n",
"# 3. Biased feature importance - features with larger scales dominate\n",
"#\n",
"# IMPORTANT: We ONLY scale numerical columns, NOT the one-hot encoded\n",
"# dummy variables. Dummy variables are already binary (0/1) and scaling\n",
"# them would distort their meaning.\n",
"# =============================================================================\n",
"\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"# Define the numerical columns to scale\n",
"# These are the original continuous features from our dataset\n",
"numerical_cols = [\n",
" 'duration', # Connection duration in seconds\n",
" 'src_bytes', # Bytes from source\n",
" 'dst_bytes', # Bytes from destination\n",
" 'count', # Connections to same host\n",
" 'srv_count', # Connections to same service\n",
" 'serror_rate', # SYN error rate\n",
" 'same_srv_rate', # Same service rate\n",
" 'dst_host_count', # Destination host connection count\n",
" 'dst_host_srv_count', # Destination host service count\n",
" 'dst_host_same_srv_rate' # Destination host same service rate\n",
"]\n",
"\n",
"# Initialize the StandardScaler\n",
"scaler = StandardScaler()\n",
"\n",
"# Fit the scaler on the numerical columns and transform them\n",
"# fit_transform() does two things:\n",
"# 1. fit: Computes the mean and std of each column\n",
"# 2. transform: Applies the formula: z = (x - mean) / std\n",
"intrusion_data[numerical_cols] = scaler.fit_transform(intrusion_data[numerical_cols])\n",
"\n",
"print(\"Scaled numerical columns. Sample data after scaling:\")\n",
"print(intrusion_data[numerical_cols].head())\n",
"print(f\"\\nVerification - Mean of scaled columns (should be ~0):\")\n",
"print(intrusion_data[numerical_cols].mean().round(6))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tuAD6NSPDiUi"
},
"source": [
"### **Why Scale Numerical Features but NOT Dummy Variables?**\n",
"\n",
"**Why scale numerical features?**\n",
"- `src_bytes` can range from 0 to millions\n",
"- `serror_rate` ranges from 0 to 1\n",
"- `duration` can range from 0 to thousands of seconds\n",
"- Without scaling, features with larger ranges would dominate the logistic regression optimization\n",
"\n",
"**Why NOT scale dummy variables?**\n",
"1. **Already Normalized:** Binary variables (0/1) naturally fall within the range [0, 1].\n",
"2. **Preserve Interpretability:** A coefficient on an unscaled dummy variable directly represents the effect of that category being present vs. absent.\n",
"3. **Avoid Distortion:** Scaling changes 0/1 to values like -0.7/1.4, which obscures the binary nature of the feature.\n",
"\n",
"**The numerical columns we scaled:**\n",
"1. `duration` - Connection length (seconds)\n",
"2. `src_bytes` - Source bytes transferred\n",
"3. `dst_bytes` - Destination bytes transferred\n",
"4. `count` - Connection count to same host\n",
"5. `srv_count` - Connection count to same service\n",
"6. `serror_rate` - SYN error rate\n",
"7. `same_srv_rate` - Same service rate\n",
"8. `dst_host_count` - Destination host connections\n",
"9. `dst_host_srv_count` - Destination service connections\n",
"10. `dst_host_same_srv_rate` - Destination same service rate"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "C5LK999eDiUi"
},
"source": [
"## Phase 3: Exploratory Data Analysis (EDA)\n",
"\n",
"Before training our model, let's visualize the data to understand patterns,\n",
"correlations, and distributions. This helps us identify potential issues\n",
"and build intuition about what features might be important for detecting attacks."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 547
},
"id": "T81PAIvhDiUi",
"outputId": "f5d01160-3f1c-4a5e-87d8-29f44cf2bdcb"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 2000x1200 with 2 Axes>"
],
"image/png": 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},
"metadata": {}
}
],
"source": [
"# =============================================================================\n",
"# CORRELATION HEATMAP\n",
"# =============================================================================\n",
"# A correlation matrix shows how strongly each pair of features is related.\n",
"# Values range from -1 to 1:\n",
"# +1 = Perfect positive correlation (both increase together)\n",
"# -1 = Perfect negative correlation (one increases, other decreases)\n",
"# 0 = No linear relationship\n",
"#\n",
"# For intrusion detection, we look for:\n",
"# - Features highly correlated with is_attack (good predictors!)\n",
"# - Features highly correlated with each other (potential multicollinearity)\n",
"#\n",
"# Note: We use .select_dtypes() to only include numeric columns in the\n",
"# correlation matrix, avoiding errors from any remaining non-numeric data.\n",
"# =============================================================================\n",
"\n",
"corr_matrix = intrusion_data.corr(numeric_only=True)\n",
"plt.figure(figsize=(20, 12))\n",
"sns.heatmap(corr_matrix, annot=True, cmap=\"coolwarm\", fmt='.2f', linewidths=0.5)\n",
"plt.title(\"Correlation Heatmap - Network Intrusion Features\", fontsize=16)\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 808
},
"id": "UwT5Mc70DiUi",
"outputId": "219910a3-0ed9-4ffc-820d-e7082b572c21"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 2000x2000 with 30 Axes>"
],
"image/png": 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},
"metadata": {}
}
],
"source": [
"# =============================================================================\n",
"# HISTOGRAMS FOR ALL COLUMNS\n",
"# =============================================================================\n",
"# Histograms show the distribution (frequency) of values in each feature.\n",
"# For cybersecurity data, we can observe:\n",
"# - Skewed distributions (e.g., most connections have low duration)\n",
"# - Binary patterns in dummy variables (bars at 0 and 1)\n",
"# - Class imbalance in the target variable (is_attack)\n",
"#\n",
"# These visualizations help us understand the \"shape\" of our data before\n",
"# feeding it into the model.\n",
"# =============================================================================\n",
"\n",
"intrusion_data.hist(figsize=(20, 20), bins=15)\n",
"plt.suptitle(\"Histograms for All Network Intrusion Features\", fontsize=16)\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IH6lArcbDiUj"
},
"source": [
"## Phase 4: Model Training\n",
"\n",
"Now that our data is preprocessed and explored, we can:\n",
"1. Split the data into training and testing sets\n",
"2. Train a logistic regression model\n",
"3. Use the trained model to make predictions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BxWXk199DiUj",
"outputId": "0667f887-a8ac-498d-ce04-bef371697915"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Data split into training and testing sets.\n",
"Training set size: 36,000 samples\n",
"Testing set size: 9,000 samples\n",
"\n",
"Training set attack ratio: 80.3%\n",
"Testing set attack ratio: 80.3%\n",
"(Ratios should be approximately equal — stratification working correctly)\n"
]
}
],
"source": [
"# =============================================================================\n",
"# TRAIN-TEST SPLIT\n",
"# =============================================================================\n",
"# We split the data into two parts:\n",
"# - Training set (80%): Used to train the model (learn patterns)\n",
"# - Testing set (20%): Used to evaluate the model (test on unseen data)\n",
"#\n",
"# WHY SPLIT?\n",
"# If we train and evaluate on the same data, the model might just memorize\n",
"# the training data (overfitting) instead of learning generalizable patterns.\n",
"# The test set simulates \"new, unseen\" network traffic.\n",
"#\n",
"# KEY PARAMETERS:\n",
"# - test_size=0.2: 20% of data reserved for testing\n",
"# - random_state=42: Ensures reproducibility (same split every time)\n",
"# - stratify=y: Maintains the same attack/normal ratio in both sets\n",
"# This is CRITICAL for imbalanced datasets in cybersecurity!\n",
"# =============================================================================\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# Separate features (X) from the target variable (y)\n",
"X = intrusion_data.drop(columns=['is_attack']) # All columns except the target\n",
"y = intrusion_data['is_attack'] # Just the target column\n",
"\n",
"# Split the data\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y,\n",
" test_size=0.2, # 20% for testing\n",
" random_state=42, # Reproducibility\n",
" stratify=y # Maintain attack/normal ratio\n",
")\n",
"\n",
"print(\"Data split into training and testing sets.\")\n",
"print(f\"Training set size: {X_train.shape[0]:,} samples\")\n",
"print(f\"Testing set size: {X_test.shape[0]:,} samples\")\n",
"print(f\"\\nTraining set attack ratio: {y_train.mean()*100:.1f}%\")\n",
"print(f\"Testing set attack ratio: {y_test.mean()*100:.1f}%\")\n",
"print(\"(Ratios should be approximately equal — stratification working correctly)\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zrmzyFunDiUj",
"outputId": "5c9bfa4a-709c-4842-82d3-6d1ec3075f74"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Logistic regression model trained successfully!\n",
"Number of features used: 27\n",
"Number of training samples: 36,000\n"
]
}
],
"source": [
"# =============================================================================\n",
"# TRAIN THE LOGISTIC REGRESSION MODEL\n",
"# =============================================================================\n",
"# LogisticRegression() creates the model with these parameters:\n",
"# - max_iter=1000: Maximum number of optimization iterations\n",
"# (default is 100, but complex datasets may need more to converge)\n",
"# - random_state=42: Ensures reproducible results\n",
"#\n",
"# .fit(X_train, y_train) is where the actual learning happens:\n",
"# - The model finds the optimal coefficients (beta values) that minimize\n",
"# the log-loss function\n",
"# - It learns which features are associated with attacks vs. normal traffic\n",
"# - After training, the model can predict probabilities for new connections\n",
"# =============================================================================\n",
"\n",
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"# Initialize the logistic regression model\n",
"log_reg = LogisticRegression(max_iter=1000, random_state=42)\n",
"\n",
"# Train the model on the training data\n",
"log_reg.fit(X_train, y_train)\n",
"\n",
"print(\"Logistic regression model trained successfully!\")\n",
"print(f\"Number of features used: {X_train.shape[1]}\")\n",
"print(f\"Number of training samples: {X_train.shape[0]:,}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EmlSV_eYDiUj"
},
"source": [
"## Phase 5: Model Evaluation\n",
"\n",
"Now we evaluate how well our trained model performs on the **test set** — data it has never seen before. This tells us how the model would perform on real, new network traffic."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pHhZJZPSDiUj",
"outputId": "7b29c4b5-ba6f-4257-8597-4156963c6679"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"==================================================\n",
"MODEL EVALUATION RESULTS\n",
"==================================================\n",
"Accuracy: 0.9911 (99.11%)\n",
"Precision: 0.9980 (99.80%)\n",
"Recall: 0.9909 (99.09%)\n",
"==================================================\n",
"\n",
"Confusion Matrix:\n",
"[[1758 14]\n",
" [ 66 7162]]\n"
]
}
],
"source": [
"# =============================================================================\n",
"# MODEL EVALUATION: PREDICTIONS AND METRICS\n",
"# =============================================================================\n",
"# We evaluate the model using three key metrics:\n",
"#\n",
"# 1. ACCURACY: What percentage of ALL predictions were correct?\n",
"# Formula: (True Positives + True Negatives) / Total\n",
"#\n",
"# 2. PRECISION: Of all connections flagged as ATTACKS, how many actually were?\n",
"# Formula: True Positives / (True Positives + False Positives)\n",
"# High precision = few false alarms\n",
"#\n",
"# 3. RECALL: Of all ACTUAL attacks, how many did we catch?\n",
"# Formula: True Positives / (True Positives + False Negatives)\n",
"# High recall = few missed attacks\n",
"#\n",
"# In cybersecurity, RECALL is often more important than precision because\n",
"# a missed attack (false negative) can be catastrophic, while a false alarm\n",
"# (false positive) is merely inconvenient.\n",
"# =============================================================================\n",
"\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix\n",
"\n",
"# Generate predictions on the test set\n",
"y_pred = log_reg.predict(X_test)\n",
"\n",
"# Calculate evaluation metrics\n",
"accuracy = accuracy_score(y_test, y_pred)\n",
"precision = precision_score(y_test, y_pred)\n",
"recall = recall_score(y_test, y_pred)\n",
"\n",
"print(\"=\" * 50)\n",
"print(\"MODEL EVALUATION RESULTS\")\n",
"print(\"=\" * 50)\n",
"print(f\"Accuracy: {accuracy:.4f} ({accuracy*100:.2f}%)\")\n",
"print(f\"Precision: {precision:.4f} ({precision*100:.2f}%)\")\n",
"print(f\"Recall: {recall:.4f} ({recall*100:.2f}%)\")\n",
"print(\"=\" * 50)\n",
"\n",
"# Confusion matrix (raw numbers)\n",
"print(\"\\nConfusion Matrix:\")\n",
"print(confusion_matrix(y_test, y_pred))"
]
},
{
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{
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"name": "stdout",
"text": [
"Confusion Matrix (Labeled):\n"
]
},
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\n"
},
"metadata": {}
}
],
"source": [
"# =============================================================================\n",
"# CONFUSION MATRIX AS A FORMATTED TABLE\n",
"# =============================================================================\n",
"# The confusion matrix is a 2x2 table that breaks down predictions into\n",
"# four categories. We format it as a labeled DataFrame for clarity.\n",
"#\n",
"# Predicted Normal | Predicted Attack\n",
"# Actual Normal | True Negative | False Positive (False Alarm)\n",
"# Actual Attack | False Negative | True Positive (Detected!)\n",
"# (Missed Attack!)\n",
"# =============================================================================\n",
"\n",
"cm = confusion_matrix(y_test, y_pred)\n",
"\n",
"# Create a nicely labeled DataFrame\n",
"cm_df = pd.DataFrame(\n",
" cm,\n",
" index=[\"Actual Normal\", \"Actual Attack\"],\n",
" columns=[\"Predicted Normal\", \"Predicted Attack\"]\n",
")\n",
"\n",
"print(\"Confusion Matrix (Labeled):\")\n",
"display(cm_df)\n",
"\n",
"# Visualize the confusion matrix as a heatmap\n",
"plt.figure(figsize=(8, 6))\n",
"sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues', linewidths=1, linecolor='black')\n",
"plt.title(\"Confusion Matrix - Network Intrusion Detection\", fontsize=14)\n",
"plt.ylabel(\"Actual Label\", fontsize=12)\n",
"plt.xlabel(\"Predicted Label\", fontsize=12)\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wGbNJwnUDiUj"
},
"source": [
"### **Understanding the Confusion Matrix in Cybersecurity Context**\n",
"\n",
"The confusion matrix breaks down model predictions into four critical categories:\n",
"\n",
"$\\text{Confusion Matrix} = \\begin{bmatrix} \\text{True Negatives (TN)} & \\text{False Positives (FP)} \\\\ \\text{False Negatives (FN)} & \\text{True Positives (TP)} \\end{bmatrix}$\n",
"\n",
"---\n",
"\n",
"### **What Each Quadrant Means for Intrusion Detection**\n",
"\n",
"1. **True Negatives (TN)** — Top-Left\n",
" - Normal traffic correctly identified as normal\n",
" - **Impact:** No alert triggered — system operates smoothly\n",
"\n",
"2. **False Positives (FP)** — Top-Right (False Alarms)\n",
" - Normal traffic incorrectly flagged as an attack\n",
" - **Impact:** Unnecessary alerts that waste analyst time. Too many false alarms lead to **alert fatigue** — analysts start ignoring alerts, which is dangerous.\n",
"\n",
"3. **False Negatives (FN)** — Bottom-Left (Missed Attacks!)\n",
" - Attack traffic incorrectly classified as normal\n",
" - **Impact:** **The most dangerous outcome!** A missed attack could lead to data breaches, service disruption, or unauthorized access. This is why **recall** is critical in cybersecurity.\n",
"\n",
"4. **True Positives (TP)** — Bottom-Right\n",
" - Attack traffic correctly identified as an attack\n",
" - **Impact:** Successful detection! The security team can respond to the threat.\n",
"\n",
"---\n",
"\n",
"### **Key Insight for Cybersecurity**\n",
"In most business applications, false positives and false negatives have roughly equal cost. In cybersecurity, **false negatives are far more costly than false positives**:\n",
"- A missed attack (FN) could mean a data breach costing millions\n",
"- A false alarm (FP) just means an analyst investigates and finds nothing\n",
"\n",
"This is why security teams often prefer models with **high recall** (catching as many attacks as possible), even at the cost of slightly lower precision (more false alarms)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "m2xV-VcRDiUj"
},
"source": [
"### **Understanding Accuracy, Precision, and Recall for Intrusion Detection**\n",
"\n",
"---\n",
"\n",
"### **1. Accuracy**\n",
"$\\text{Accuracy} = \\frac{\\text{True Positives} + \\text{True Negatives}}{\\text{Total Observations}}$\n",
"\n",
"- Measures overall correctness across ALL predictions.\n",
"- **Limitation in Cybersecurity:** If 95% of traffic is normal, a model that predicts \"normal\" for everything achieves 95% accuracy — but catches zero attacks! This is why accuracy alone is misleading for imbalanced cybersecurity datasets.\n",
"\n",
"---\n",
"\n",
"### **2. Precision**\n",
"$\\text{Precision} = \\frac{\\text{True Positives}}{\\text{True Positives} + \\text{False Positives}}$\n",
"\n",
"- Answers: **\"When the model raises an alarm, how often is it a real attack?\"**\n",
"- High precision means fewer false alarms and less alert fatigue\n",
"- **When to prioritize:** When analyst time is limited and you cannot afford to chase false alarms\n",
"\n",
"---\n",
"\n",
"### **3. Recall (Sensitivity / Detection Rate)**\n",
"$\\text{Recall} = \\frac{\\text{True Positives}}{\\text{True Positives} + \\text{False Negatives}}$\n",
"\n",
"- Answers: **\"Of all actual attacks, what percentage did the model detect?\"**\n",
"- High recall means fewer missed attacks and better security coverage\n",
"- **When to prioritize:** When the cost of missing an attack is very high (data breach, ransomware, etc.)\n",
"\n",
"---\n",
"\n",
"### **The Precision-Recall Trade-off in Security Operations**\n",
"\n",
"| Scenario | Threshold | Precision | Recall | Consequence |\n",
"|----------|-----------|-----------|--------|-------------|\n",
"| Conservative | High (0.8) | High | Low | Few alarms, but some attacks slip through |\n",
"| Balanced | Medium (0.5) | Medium | Medium | Reasonable balance of alerts and detection |\n",
"| Aggressive | Low (0.2) | Low | High | Many alarms, but almost no attacks missed |\n",
"\n",
"**Security teams must choose based on their threat model:**\n",
"- **High-value targets** (banks, government): Prioritize recall — missing attacks is unacceptable\n",
"- **High-volume environments** (ISPs, cloud providers): Balance precision and recall — too many false alarms is unsustainable"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "VcRRV2zLDiUj",
"outputId": "e58669ae-111a-41f8-fb27-5224cfc4c64f"
},
"outputs": [
{
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" Feature Coefficient\n",
"12 service_eco_i 5.696687\n",
"17 service_private 4.439733\n",
"3 count 3.697497\n",
"20 flag_RSTR 3.080715\n",
"1 src_bytes 2.150661\n",
"4 srv_count 1.744429\n",
"13 service_ecr_i 1.441751\n",
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"Top 10 features indicating NORMAL traffic (negative coefficients):\n"
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" Feature Coefficient\n",
"0 duration -0.003723\n",
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"16 service_other -0.368476\n",
"22 flag_S1 -0.399921\n",
"6 same_srv_rate -0.439301\n",
"14 service_ftp_data -0.517959\n",
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OnTIkGQsXLrSea968uREWFmbTX2ZmptGoUSOjcuXKeX4mAADyO5bvAQAA5AMpKSmSJE9Pzzy1//LLLyUpyzfCDRkyRJKse09t3LhRV65cUdeuXfXrr79aDycnJzVo0EBbtmy5X4+gVatW6aGHHtIrr7yS5ZrJZMr2HsMwtGrVKrVt21aGYdjUGBkZKYvFon379tnc8/zzz9vsZ9W4cWNJ0smTJ++69s6dO6to0aI2S/i2bt2qs2fPWpfuSZK7u7v156tXr+rXX39V48aN9dtvv+nYsWN3Pf4tly5d0ubNm9WpUydr/7/++qsuXryoyMhIHT9+XGfPnr3ncQAAyA9YvgcAAJAPeHl5Sfoj6MiL06dPq0iRIjbfhidJ/v7+Kl68uE6fPi1JOn78uCSpWbNmtx33fkhISFBISMgdfbPehQsXdOXKFc2bNy/HbxW8tdH6LeXLl7d5XaJECUnS5cuX77Di/+Pr66vIyEitWbNG77//vtzc3BQTEyNnZ2d16tTJ2u7IkSN68803tXnzZmuQeIvFYrnr8W85ceKEDMPQyJEjNXLkyGzbnD9/XmXLlr3nsQAAcDRCKQAAgHzAy8tLZcqU0eHDh+/ovpxmIN2SmZkp6Y99pfz9/bNcv5MA6UG4Vd+zzz5r3Wj9r/66h9Wf93z6M8Mw7qmWZ599Vl988YW++OILPfnkk1q1apUef/xx+fn5SZKuXLmiiIgIeXl5ady4capYsaLc3Ny0b98+vf7669ZnyU5O/05/3SD9Vh9Dhw5VZGRktvf8NYgEAKCgIpQCAADIJ9q0aaN58+bpu+++U8OGDW/bNjAwUJmZmTp+/LhCQ0Ot58+dO6crV64oMDBQkqybi5csWVItWrR4cMX//7F27typGzduqGjRonm6x8/PT56ensrIyLiv9eUW1mXnySeflKenp2JiYlS0aFFdvnzZZulebGysLl68qNWrV6tJkybW86dOncq171uzua5cuWJz/taMtlsqVKggSSpatOgD//cCAMDR2FMKAAAgn3jttdfk4eGhF154QefOnctyPSEhQTNnzpQktW7dWpI0Y8YMmzZvv/22JOmJJ56QJEVGRsrLy0uTJk3SjRs3svR54cKF+1b/008/rV9//VWzZ8/Oci2nWUxOTk56+umntWrVqmxnid1tfR4eHne8nM7d3V1PPfWUvvzyS82dO1ceHh5q166dTa2S7bOkp6drzpw5ufYdGBgoJycnbdu2zeb8X+8tWbKkmjZtqg8++EDJyclZ+rmf/14AADgaM6UAAADyiYoVKyomJkadO3dWaGiounXrpho1aig9PV3ffvutPv74Y/Xo0UOSVKtWLXXv3l3z5s2zLivbtWuXFi9erKioKD366KOS/lgWOHfuXD333HOqU6eOunTpIj8/PyUlJWndunUKDw/PNkS6G926ddNHH32kwYMHa9euXWrcuLFSU1P1zTffqG/fvjYBz59NmTJFW7ZsUYMGDdS7d29Vq1ZNly5d0r59+/TNN9/o0qVLd1xL3bp1tWLFCg0ePFj169eX2WxW27Ztc73v2Wef1UcffaQNGzYoOjpaHh4e1muNGjVSiRIl1L17dw0YMEAmk0lLlizJ07JBb29vdezYUbNmzZLJZFLFihX1xRdfZNkvS5Lee+89/fOf/1RYWJh69+6tChUq6Ny5c/ruu+/0008/6eDBg3f2ZgAAkE8RSgEAAOQjTz75pL7//ntNmzZNn332mebOnStXV1fVrFlT06dPV+/eva1tFyxYoAoVKmjRokVas2aN/P39NXz4cI0ePdqmz2eeeUZlypTRlClTNG3aNKWlpals2bJq3Lixnn/++ftWu5OTk7788ktNnDhRMTExWrVqlXx9fa0BS05KlSqlXbt2ady4cVq9erXmzJkjX19fVa9eXW+99dZd1dK3b18dOHBACxcu1DvvvKPAwMA8hVLNmjVT6dKllZycbLN0T/pjM/QvvvhCQ4YM0ZtvvqkSJUro2WefVfPmzXPc/+nPZs2apRs3buj999+Xq6urOnXqpGnTpqlGjRo27apVq6Y9e/Zo7NixWrRokS5evKiSJUuqdu3aGjVq1J29EQAA5GMm4153hAQAAAAAAADuEHtKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAAAAACwO0IpAAAAAAAA2B2hFAAAAAAAAOyOUAoAAAAAAAB2RygFAAAAAAAAuyOUAgAAAAAAgN0RSgEAAAAAAMDuCKUAAAAAAABgd4RSAAAAAAAAsDtCKQAAAAAAANgdoRQAAAAAAADs7v8Bk5wkJ8pFZw0AAAAASUVORK5CYII=\n"
},
"metadata": {}
}
],
"source": [
"# =============================================================================\n",
"# FEATURE IMPORTANCE: WHICH FEATURES MATTER MOST?\n",
"# =============================================================================\n",
"# One of logistic regression's key strengths is interpretability.\n",
"# The model's coefficients tell us how much each feature influences the\n",
"# prediction of an attack.\n",
"#\n",
"# Positive coefficient -> increases probability of attack classification\n",
"# Negative coefficient -> decreases probability of attack classification\n",
"#\n",
"# This is valuable for security analysts: it explains WHY the model\n",
"# flagged a connection as malicious.\n",
"# =============================================================================\n",
"\n",
"# Get feature names and their coefficients\n",
"feature_importance = pd.DataFrame({\n",
" 'Feature': X_train.columns,\n",
" 'Coefficient': log_reg.coef_[0]\n",
"}).sort_values('Coefficient', ascending=False)\n",
"\n",
"# Display top features that indicate ATTACK\n",
"print(\"Top 10 features indicating ATTACK (positive coefficients):\")\n",
"display(feature_importance.head(10))\n",
"\n",
"# Display top features that indicate NORMAL traffic (negative coefficients)\n",
"print(\"\\nTop 10 features indicating NORMAL traffic (negative coefficients):\")\n",
"display(feature_importance.tail(10))\n",
"\n",
"# Visualize feature importance\n",
"plt.figure(figsize=(12, 8))\n",
"top_features = pd.concat([feature_importance.head(10), feature_importance.tail(10)])\n",
"colors = ['red' if x > 0 else 'green' for x in top_features['Coefficient']]\n",
"plt.barh(top_features['Feature'], top_features['Coefficient'], color=colors)\n",
"plt.xlabel('Coefficient Value', fontsize=12)\n",
"plt.title('Logistic Regression Feature Importance\\n(Red = Attack Indicator, Green = Normal Indicator)', fontsize=14)\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "V-0Nk4ljDiUj"
},
"source": [
"## Phase 6: Operationalize with Streamlit\n",
"\n",
"Now let's deploy our intrusion detection model as an interactive web application using **Streamlit**! This allows security analysts (or anyone) to input network connection features and get real-time predictions.\n",
"\n",
"### Steps to Run Streamlit in Google Colab\n",
"1. **Install Streamlit and ngrok** — Colab doesn't have Streamlit pre-installed, and ngrok creates a public URL since Colab doesn't expose ports directly\n",
"2. **Configure ngrok** — Set up authentication for the tunnel service\n",
"3. **Write the Streamlit app** — Create the interactive web interface\n",
"4. **Run and access** — Launch the app and open it via the public URL\n",
"\n",
"### Caveats\n",
"- Each time you restart the Colab runtime, you'll need to re-run all cells\n",
"- The app runs temporarily and stops when the Colab session ends\n",
"- ngrok free tier has a session time limit"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7shlEKjcDiUj",
"outputId": "c2f844e3-7b66-4f30-f7b4-c509135c3bfe"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting streamlit\n",
" Downloading streamlit-1.55.0-py3-none-any.whl.metadata (9.8 kB)\n",
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"\u001b[?25hInstalling collected packages: pydeck, streamlit\n",
"Successfully installed pydeck-0.9.1 streamlit-1.55.0\n",
"Collecting pyngrok\n",
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"Installing collected packages: pyngrok\n",
"Successfully installed pyngrok-7.5.1\n"
]
}
],
"source": [
"# =============================================================================\n",
"# INSTALL REQUIRED PACKAGES FOR DEPLOYMENT\n",
"# =============================================================================\n",
"# streamlit: Framework for building interactive web applications with Python\n",
"# pyngrok: Python wrapper for ngrok, which creates secure tunnels to localhost\n",
"#\n",
"# In Google Colab, we use !pip to install packages into the runtime environment.\n",
"# The '!' prefix runs the command in the system shell (not Python).\n",
"# =============================================================================\n",
"\n",
"!pip install streamlit\n",
"!pip install pyngrok"
]
},
{
"cell_type": "markdown",
"source": [
"\n",
"1. **Sign Up for ngrok**:\n",
" - Go to the [ngrok signup page](https://dashboard.ngrok.com/signup) and create a free account if you don't already have one.\n",
"\n",
"2. **Get Your ngrok Auth Token**:\n",
" - After logging in, go to the [ngrok dashboard](https://dashboard.ngrok.com/get-started/your-authtoken).\n",
" - Copy your personal authentication token from this page.\n",
"\n",
"3. **Set the Auth Token in Colab**:\n",
" - In your Colab notebook, before starting the Streamlit app, authenticate ngrok by running:\n",
" ```python\n",
" !ngrok config add-authtoken YOUR_AUTH_TOKEN\n",
" ```\n",
" Replace `YOUR_AUTH_TOKEN` with the token you copied from the ngrok dashboard.\n",
"\n",
"4. **Restart the App**:\n",
" - After authenticating, rerun the Streamlit app code. The public URL should now be generated successfully.\n",
"\n",
"---"
],
"metadata": {
"id": "TfMPt4arO5FV"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vM1ueMCuDiUk",
"outputId": "4741da83-2f30-4bcd-f069-4b5a5b721a3a"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Enter your ngrok auth token: ··········\n",
"Authtoken saved to configuration file: /root/.config/ngrok/ngrok.yml\n"
]
}
],
"source": [
"# =============================================================================\n",
"# CONFIGURE NGROK AUTHENTICATION\n",
"# =============================================================================\n",
"# ngrok requires a free authentication token to create tunnels.\n",
"# Steps to get your token:\n",
"# 1. Go to https://ngrok.com and create a free account\n",
"# 2. Navigate to \"Your Authtoken\" in the dashboard\n",
"# 3. Copy the token and paste it when prompted below\n",
"#\n",
"# getpass.getpass() hides the input so your token isn't displayed in the notebook.\n",
"# This is a security best practice — never hardcode tokens in your code!\n",
"# =============================================================================\n",
"\n",
"import getpass\n",
"ngrok_token = getpass.getpass(\"Enter your ngrok auth token: \")\n",
"!ngrok config add-authtoken $ngrok_token"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sm2InLz6DiUk",
"outputId": "6e6c7205-8461-4719-80ba-5b701a47503c"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Streamlit app is live at: NgrokTunnel: \"https://1a75-34-125-156-99.ngrok-free.app\" -> \"http://localhost:8501\"\n",
"Click the URL above to open the Network Intrusion Detection app!\n"
]
}
],
"source": [
"# =============================================================================\n",
"# WRITE AND LAUNCH THE STREAMLIT APPLICATION\n",
"# =============================================================================\n",
"# This cell does three things:\n",
"# 1. Writes the Streamlit app code to a file called \"app.py\"\n",
"# 2. Starts the Streamlit server as a background process\n",
"# 3. Creates a public URL using ngrok so you can access the app\n",
"#\n",
"# The app code includes:\n",
"# - Data loading and model training (self-contained)\n",
"# - Interactive sidebar with input sliders and dropdowns\n",
"# - Real-time prediction display with probability scores\n",
"# =============================================================================\n",
"\n",
"import subprocess\n",
"from pyngrok import ngrok\n",
"\n",
"# Write the Streamlit app code to a file\n",
"with open(\"app.py\", \"w\") as f:\n",
" f.write(\"\"\"\n",
"import streamlit as st\n",
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.datasets import fetch_kddcup99\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# -------------------------------------------------------------------------\n",
"# DATA LOADING AND MODEL TRAINING\n",
"# -------------------------------------------------------------------------\n",
"# We repeat the data loading and preprocessing here because the Streamlit\n",
"# app runs as a separate Python process from the notebook.\n",
"# In a production environment, you would save the trained model to a file\n",
"# (using joblib or pickle) and load it in the app instead.\n",
"# -------------------------------------------------------------------------\n",
"\n",
"@st.cache_data\n",
"def load_and_train():\n",
" # Load data and train model. Cached so it only runs once.\n",
"\n",
" # Load KDD Cup 99 dataset\n",
" kdd = fetch_kddcup99(subset=None, percent10=True, as_frame=True)\n",
" network_data = kdd['data'].copy()\n",
" network_data['label'] = kdd['target']\n",
"\n",
" # Convert numerical columns from object dtype to proper numeric types\n",
" categorical_cols_raw = ['protocol_type', 'service', 'flag']\n",
" for col in network_data.columns:\n",
" if col not in categorical_cols_raw and col != 'label':\n",
" network_data[col] = pd.to_numeric(network_data[col], errors='coerce')\n",
"\n",
" # Create binary target\n",
" network_data['is_attack'] = (network_data['label'] != b'normal.').astype(int)\n",
"\n",
" # Select features\n",
" selected_features = [\n",
" 'duration', 'src_bytes', 'dst_bytes', 'count', 'srv_count',\n",
" 'serror_rate', 'same_srv_rate', 'dst_host_count',\n",
" 'dst_host_srv_count', 'dst_host_same_srv_rate',\n",
" 'protocol_type', 'service', 'flag',\n",
" ]\n",
"\n",
" intrusion_data = network_data[selected_features + ['is_attack']].copy()\n",
"\n",
" # Decode byte strings\n",
" for col in ['protocol_type', 'service', 'flag']:\n",
" intrusion_data[col] = intrusion_data[col].apply(\n",
" lambda x: x.decode() if isinstance(x, bytes) else str(x)\n",
" )\n",
"\n",
" # Reduce service cardinality\n",
" top_services = intrusion_data['service'].value_counts().head(8).index.tolist()\n",
" intrusion_data['service'] = intrusion_data['service'].apply(\n",
" lambda x: x if x in top_services else 'other'\n",
" )\n",
"\n",
" # Sample\n",
" intrusion_data, _ = train_test_split(\n",
" intrusion_data, train_size=45000, random_state=42,\n",
" stratify=intrusion_data['is_attack']\n",
" )\n",
" intrusion_data = intrusion_data.reset_index(drop=True)\n",
"\n",
" # One-hot encode\n",
" intrusion_data = pd.get_dummies(intrusion_data, drop_first=True)\n",
" boolean_cols = intrusion_data.select_dtypes(include=['bool']).columns\n",
" intrusion_data[boolean_cols] = intrusion_data[boolean_cols].astype(int)\n",
"\n",
" # Scale numerical features\n",
" numerical_cols = [\n",
" 'duration', 'src_bytes', 'dst_bytes', 'count', 'srv_count',\n",
" 'serror_rate', 'same_srv_rate', 'dst_host_count',\n",
" 'dst_host_srv_count', 'dst_host_same_srv_rate'\n",
" ]\n",
" scaler = StandardScaler()\n",
" intrusion_data[numerical_cols] = scaler.fit_transform(intrusion_data[numerical_cols])\n",
"\n",
" # Train model\n",
" X = intrusion_data.drop(columns=['is_attack'])\n",
" y = intrusion_data['is_attack']\n",
" model = LogisticRegression(max_iter=1000, random_state=42)\n",
" model.fit(X, y)\n",
"\n",
" return model, scaler, X.columns.tolist(), numerical_cols, top_services\n",
"\n",
"# Load model and preprocessing objects\n",
"model, scaler, feature_columns, numerical_cols, top_services = load_and_train()\n",
"\n",
"# -------------------------------------------------------------------------\n",
"# STREAMLIT USER INTERFACE\n",
"# -------------------------------------------------------------------------\n",
"st.title(\"Network Intrusion Detection System\")\n",
"st.markdown(\"#### Powered by Logistic Regression\")\n",
"st.markdown(\"Enter network connection features below to predict if the traffic is **normal** or an **attack**.\")\n",
"\n",
"st.sidebar.header(\"Network Connection Features\")\n",
"\n",
"# --- Numerical Feature Inputs ---\n",
"st.sidebar.subheader(\"Connection Metrics\")\n",
"duration = st.sidebar.slider(\"Connection Duration (seconds):\", 0, 60000, 0)\n",
"src_bytes = st.sidebar.slider(\"Source Bytes:\", 0, 1000000, 0)\n",
"dst_bytes = st.sidebar.slider(\"Destination Bytes:\", 0, 1000000, 0)\n",
"\n",
"st.sidebar.subheader(\"Traffic Patterns\")\n",
"count = st.sidebar.slider(\"Connections to Same Host (last 2s):\", 0, 511, 1)\n",
"srv_count = st.sidebar.slider(\"Connections to Same Service (last 2s):\", 0, 511, 1)\n",
"serror_rate = st.sidebar.slider(\"SYN Error Rate:\", 0.0, 1.0, 0.0, step=0.01)\n",
"same_srv_rate = st.sidebar.slider(\"Same Service Rate:\", 0.0, 1.0, 1.0, step=0.01)\n",
"\n",
"st.sidebar.subheader(\"Destination Host Stats\")\n",
"dst_host_count = st.sidebar.slider(\"Dest Host Connection Count:\", 0, 255, 0)\n",
"dst_host_srv_count = st.sidebar.slider(\"Dest Host Service Count:\", 0, 255, 0)\n",
"dst_host_same_srv_rate = st.sidebar.slider(\"Dest Host Same Service Rate:\", 0.0, 1.0, 0.0, step=0.01)\n",
"\n",
"# --- Categorical Feature Inputs ---\n",
"st.sidebar.subheader(\"Connection Type\")\n",
"protocol_type = st.sidebar.selectbox(\"Protocol Type:\", [\"tcp\", \"udp\", \"icmp\"])\n",
"service = st.sidebar.selectbox(\"Network Service:\", sorted(top_services + ['other']))\n",
"flag_options = [\"SF\", \"S0\", \"REJ\", \"RSTR\", \"RSTO\", \"SH\", \"S1\", \"S2\", \"S3\", \"OTH\", \"RSTOS0\"]\n",
"flag = st.sidebar.selectbox(\"Connection Flag:\", flag_options)\n",
"\n",
"# -------------------------------------------------------------------------\n",
"# BUILD INPUT DATAFRAME WITH PROPER ENCODING\n",
"# -------------------------------------------------------------------------\n",
"# We need to create a DataFrame that matches the exact feature columns\n",
"# the model was trained on, including all one-hot encoded columns.\n",
"\n",
"# Start with all zeros for every feature column\n",
"input_dict = {col: [0] for col in feature_columns}\n",
"\n",
"# Set numerical values (raw, will be scaled below)\n",
"input_dict['duration'] = [duration]\n",
"input_dict['src_bytes'] = [src_bytes]\n",
"input_dict['dst_bytes'] = [dst_bytes]\n",
"input_dict['count'] = [count]\n",
"input_dict['srv_count'] = [srv_count]\n",
"input_dict['serror_rate'] = [serror_rate]\n",
"input_dict['same_srv_rate'] = [same_srv_rate]\n",
"input_dict['dst_host_count'] = [dst_host_count]\n",
"input_dict['dst_host_srv_count'] = [dst_host_srv_count]\n",
"input_dict['dst_host_same_srv_rate'] = [dst_host_same_srv_rate]\n",
"\n",
"# Set one-hot encoded categorical values\n",
"for pt in ['tcp', 'udp']:\n",
" col_name = f'protocol_type_{pt}'\n",
" if col_name in feature_columns:\n",
" input_dict[col_name] = [1 if protocol_type == pt else 0]\n",
"\n",
"for svc in top_services + ['other']:\n",
" col_name = f'service_{svc}'\n",
" if col_name in feature_columns:\n",
" input_dict[col_name] = [1 if service == svc else 0]\n",
"\n",
"for f in flag_options:\n",
" col_name = f'flag_{f}'\n",
" if col_name in feature_columns:\n",
" input_dict[col_name] = [1 if flag == f else 0]\n",
"\n",
"input_data = pd.DataFrame(input_dict)\n",
"\n",
"# Scale the numerical columns using the same scaler from training\n",
"input_data[numerical_cols] = scaler.transform(input_data[numerical_cols])\n",
"\n",
"# Ensure column order matches training data\n",
"input_data = input_data[feature_columns]\n",
"\n",
"# -------------------------------------------------------------------------\n",
"# MAKE PREDICTION AND DISPLAY RESULTS\n",
"# -------------------------------------------------------------------------\n",
"prediction = model.predict(input_data)[0]\n",
"probability = model.predict_proba(input_data)[0]\n",
"\n",
"st.markdown(\"---\")\n",
"st.subheader(\"Prediction Result\")\n",
"\n",
"if prediction == 1:\n",
" st.error(\"ALERT: This connection is classified as an **ATTACK**\")\n",
" st.metric(\"Attack Probability\", f\"{probability[1]*100:.1f}%\")\n",
"else:\n",
" st.success(\"This connection is classified as **NORMAL**\")\n",
" st.metric(\"Normal Probability\", f\"{probability[0]*100:.1f}%\")\n",
"\n",
"# Show probability breakdown\n",
"st.markdown(\"#### Probability Breakdown\")\n",
"col1, col2 = st.columns(2)\n",
"col1.metric(\"Normal\", f\"{probability[0]*100:.1f}%\")\n",
"col2.metric(\"Attack\", f\"{probability[1]*100:.1f}%\")\n",
"\n",
"# Show the raw input features\n",
"with st.expander(\"View Input Features (Raw)\"):\n",
" st.write(f\"Protocol: {protocol_type} | Service: {service} | Flag: {flag}\")\n",
" st.write(f\"Duration: {duration}s | Src Bytes: {src_bytes} | Dst Bytes: {dst_bytes}\")\n",
" st.write(f\"Count: {count} | Srv Count: {srv_count}\")\n",
" st.write(f\"SYN Error Rate: {serror_rate} | Same Srv Rate: {same_srv_rate}\")\n",
"\"\"\")\n",
"\n",
"# Start the Streamlit app as a background process\n",
"command = [\"streamlit\", \"run\", \"app.py\", \"--server.port\", \"8501\"]\n",
"process = subprocess.Popen(command)\n",
"\n",
"# Create a public URL using ngrok\n",
"public_url = ngrok.connect(8501)\n",
"print(f\"\\nStreamlit app is live at: {public_url}\")\n",
"process.terminate()\n",
"print(\"Click the URL above to open the Network Intrusion Detection app!\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aA8ZVdN7DiUk"
},
"source": [
"### **Implementation Overview**\n",
"\n",
"Here is a summary of the complete pipeline we built:\n",
"\n",
"| Phase | Step | Why |\n",
"|-------|------|-----|\n",
"| **1. Data Loading** | Load KDD Cup 99 from sklearn | Well-known benchmark dataset for intrusion detection |\n",
"| **2. Feature Selection** | Select 13 key features | Manageable set of interpretable features |\n",
"| **3. Target Creation** | Binary: normal (0) vs attack (1) | Simplify multi-class to binary for logistic regression |\n",
"| **4. Encoding** | One-hot encode categorical features | Convert protocol, service, flag to numeric format |\n",
"| **5. Scaling** | StandardScaler on numerical features | Normalize feature ranges for gradient optimization |\n",
"| **6. Split** | 80/20 train/test with stratification | Evaluate on unseen data while preserving class balance |\n",
"| **7. Train** | LogisticRegression with max_iter=1000 | Learn decision boundary between normal and attack traffic |\n",
"| **8. Evaluate** | Accuracy, Precision, Recall, Confusion Matrix | Measure model performance from multiple angles |\n",
"| **9. Deploy** | Streamlit + ngrok | Interactive web app for real-time predictions |\n",
"\n",
"### **Key Takeaways for Cybersecurity**\n",
"- **Logistic regression is interpretable** — you can explain WHY a connection was flagged\n",
"- **Recall matters most** — missing attacks has higher cost than false alarms\n",
"- **Feature engineering is critical** — the right features make or break detection\n",
"- **Baseline models matter** — logistic regression sets the foundation before trying complex models"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wd2aez19DiUk"
},
"outputs": [],
"source": [
"# =============================================================================\n",
"# STOP THE STREAMLIT APP\n",
"# =============================================================================\n",
"# Run this cell when you're done using the Streamlit app.\n",
"# This terminates the background Streamlit process and frees up resources.\n",
"# =============================================================================\n",
"\n",
"process.terminate()\n",
"print(\"Streamlit app stopped.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PNdSIxfwDiUk"
},
"source": [
"---\n",
"## Student Exercises\n",
"\n",
"Complete the following exercises to deepen your understanding of logistic regression for cybersecurity applications. Each exercise builds on the concepts covered in this lesson."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "isszgcfcDiUk"
},
"source": [
"### **Exercise 1: Threshold Tuning for Security Operations**\n",
"\n",
"In cybersecurity, the default classification threshold of 0.5 may not be optimal.\n",
"A security operations center (SOC) might prefer different thresholds depending on their priorities.\n",
"\n",
"**Task:** Modify the classification threshold and observe how it affects precision and recall.\n",
"Try thresholds of 0.3, 0.5, and 0.7. Which would you recommend for a high-security environment? Why?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qOtM497EDiUk",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0673456c-cdeb-47ce-8cfa-43a83b688058"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Threshold: 0.3\n",
" Precision: 0.9956\n",
" Recall: 0.9939\n",
"Threshold: 0.5\n",
" Precision: 0.9980\n",
" Recall: 0.9909\n",
"Threshold: 0.7\n",
" Precision: 0.9982\n",
" Recall: 0.9869\n"
]
}
],
"source": [
"# =============================================================================\n",
"# EXERCISE 1: THRESHOLD TUNING\n",
"# =============================================================================\n",
"# TODO: Complete the code below to evaluate the model at different thresholds.\n",
"#\n",
"# HINTS:\n",
"# - Use log_reg.predict_proba(X_test) to get probability scores\n",
"# - The second column [:,1] gives the probability of being an attack\n",
"# - Use (probabilities >= threshold).astype(int) to apply a custom threshold\n",
"# - Calculate precision and recall for each threshold\n",
"# =============================================================================\n",
"\n",
"from sklearn.metrics import precision_score, recall_score, f1_score\n",
"\n",
"# Get probability scores (not just 0/1 predictions)\n",
"y_proba = log_reg.predict_proba(X_test)[:, 1] # Probability of attack class\n",
"# print(y_proba[:20])\n",
"thresholds = [0.3, 0.5, 0.7]\n",
"\n",
"\n",
"for threshold in thresholds:\n",
" # TODO: Apply the threshold to convert probabilities to predictions\n",
" y_pred_custom = (y_proba >= threshold).astype(int)\n",
" # print(y_pred_custom)\n",
"\n",
"# # TODO: Calculate precision and recall\n",
" # accuracy = accuracy_score(y_test, y_pred)\n",
" precision = precision_score(y_test, y_pred_custom) # FALSE POSITVE ---\n",
" recall = recall_score(y_test, y_pred_custom) # FALSE NEGATIVE --\n",
"# # TODO: Print the results for each threshold\n",
" print(f\"Threshold: {threshold}\")\n",
" print(f\" Precision: {precision:.4f}\")\n",
" print(f\" Recall: {recall:.4f}\")\n",
" pass\n",
"\n",
"# TODO: In a markdown cell or comment below, answer:\n",
"# Which threshold would you recommend for a high-security environment and why?\n",
"# YOUR ANSWER:\n",
"#"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "s0VTa5qtDiUk"
},
"source": [
"### **Exercise 2: Feature Importance Analysis**\n",
"\n",
"Understanding which features contribute most to attack detection is crucial for:\n",
"- Building better detection rules\n",
"- Understanding attack patterns\n",
"- Explaining alerts to stakeholders\n",
"\n",
"**Task:** Identify the top 5 most important features for detecting attacks and explain what each one means in a cybersecurity context. What attack types might each feature help detect?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jhdeP6KBDiUk"
},
"outputs": [],
"source": [
"# =============================================================================\n",
"# EXERCISE 2: FEATURE IMPORTANCE DEEP DIVE\n",
"# =============================================================================\n",
"# TODO: Complete the code to analyze and interpret feature importance.\n",
"#\n",
"# HINTS:\n",
"# - log_reg.coef_[0] contains the coefficient for each feature\n",
"# - Larger absolute values = more important features\n",
"# - Positive values = feature increases attack probability\n",
"# - Negative values = feature decreases attack probability\n",
"# =============================================================================\n",
"\n",
"# TODO: Create a DataFrame of feature names and their coefficients\n",
"# feature_df = pd.DataFrame({...})\n",
"\n",
"# TODO: Sort by absolute coefficient value to find the most important features\n",
"# feature_df['abs_coeff'] = ???\n",
"# feature_df = feature_df.sort_values(???)\n",
"\n",
"# TODO: Display the top 5 most important features\n",
"# print(\"Top 5 Most Important Features for Attack Detection:\")\n",
"# display(feature_df.head(5))\n",
"\n",
"# TODO: For each of the top 5 features, write a comment explaining:\n",
"# 1. What the feature measures in network traffic\n",
"# 2. Why it might be important for detecting attacks\n",
"# 3. What types of attacks it might help identify (DoS, probe, R2L, U2R)\n",
"#\n",
"# YOUR ANALYSIS:\n",
"#"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7d7EGfX9DiUl"
},
"source": [
"### **Exercise 3: Class Imbalance Investigation**\n",
"\n",
"Real-world network traffic is heavily imbalanced — attacks are rare compared to normal traffic. This can bias the model toward predicting \"normal\" for everything.\n",
"\n",
"**Task:** Retrain the model using `class_weight='balanced'` and compare the results with the original model. How does handling class imbalance affect precision vs. recall?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DihNqN71DiUl"
},
"outputs": [],
"source": [
"# =============================================================================\n",
"# EXERCISE 3: HANDLING CLASS IMBALANCE\n",
"# =============================================================================\n",
"# TODO: Retrain the model with class_weight='balanced' and compare results.\n",
"#\n",
"# HINTS:\n",
"# - LogisticRegression(max_iter=1000, random_state=42, class_weight='balanced')\n",
"# - class_weight='balanced' automatically adjusts weights inversely proportional\n",
"# to class frequencies: weight = n_samples / (n_classes * count_per_class)\n",
"# - This makes the model pay more attention to the minority class (attacks)\n",
"# - Compare accuracy, precision, and recall with the original model\n",
"# =============================================================================\n",
"\n",
"# TODO: Train a new model with balanced class weights\n",
"# log_reg_balanced = LogisticRegression(???)\n",
"# log_reg_balanced.fit(???)\n",
"\n",
"# TODO: Make predictions with the balanced model\n",
"# y_pred_balanced = ???\n",
"\n",
"# TODO: Calculate and print metrics for both models side by side\n",
"# print(\"Original Model:\")\n",
"# print(f\" Accuracy: {???:.4f}\")\n",
"# print(f\" Precision: {???:.4f}\")\n",
"# print(f\" Recall: {???:.4f}\")\n",
"# print()\n",
"# print(\"Balanced Model:\")\n",
"# print(f\" Accuracy: {???:.4f}\")\n",
"# print(f\" Precision: {???:.4f}\")\n",
"# print(f\" Recall: {???:.4f}\")\n",
"\n",
"# TODO: Answer these questions in comments:\n",
"# 1. How did class_weight='balanced' affect recall?\n",
"# 2. How did it affect precision?\n",
"# 3. Which model would you deploy in a real SOC and why?\n",
"#\n",
"# YOUR ANSWERS:\n",
"#"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ooafomVKDiUl"
},
"source": [
"### **Exercise 4: ROC Curve and AUC Score**\n",
"\n",
"The ROC (Receiver Operating Characteristic) curve is a key tool for evaluating binary classifiers in cybersecurity. It shows the trade-off between the True Positive Rate (recall) and the False Positive Rate across all possible thresholds.\n",
"\n",
"**Task:** Plot the ROC curve for the model and calculate the AUC (Area Under the Curve) score. What does the AUC tell us about the model's overall detection capability?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "V8AmFYhbDiUl"
},
"outputs": [],
"source": [
"# =============================================================================\n",
"# EXERCISE 4: ROC CURVE AND AUC\n",
"# =============================================================================\n",
"# TODO: Plot the ROC curve and calculate AUC score.\n",
"#\n",
"# HINTS:\n",
"# - from sklearn.metrics import roc_curve, roc_auc_score\n",
"# - roc_curve(y_test, y_proba) returns fpr, tpr, thresholds\n",
"# - roc_auc_score(y_test, y_proba) returns the AUC score\n",
"# - Plot fpr vs tpr for the ROC curve\n",
"# - A diagonal line from (0,0) to (1,1) represents a random classifier\n",
"# - AUC of 1.0 = perfect model, 0.5 = random guessing\n",
"# =============================================================================\n",
"\n",
"from sklearn.metrics import roc_curve, roc_auc_score\n",
"\n",
"# TODO: Get probability scores for the positive class (attack)\n",
"# y_proba = ???\n",
"\n",
"# TODO: Calculate ROC curve values\n",
"# fpr, tpr, thresholds = roc_curve(???)\n",
"\n",
"# TODO: Calculate AUC score\n",
"# auc_score = roc_auc_score(???)\n",
"\n",
"# TODO: Plot the ROC curve\n",
"# plt.figure(figsize=(8, 6))\n",
"# plt.plot(fpr, tpr, label=f'Logistic Regression (AUC = {auc_score:.4f})')\n",
"# plt.plot([0, 1], [0, 1], 'k--', label='Random Classifier')\n",
"# plt.xlabel('False Positive Rate')\n",
"# plt.ylabel('True Positive Rate (Recall)')\n",
"# plt.title('ROC Curve - Network Intrusion Detection')\n",
"# plt.legend()\n",
"# plt.show()\n",
"\n",
"# TODO: Answer: What does your AUC score tell you about the model?\n",
"# Is it suitable for production use in a SOC? Why or why not?\n",
"#\n",
"# YOUR ANSWER:\n",
"#"
]
}
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