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Five required + four optional slides, slotted into the existing flow without renumbering the visible deck UI: REQUIRED - problem-statement (after motivation): single-sentence problem, three numeric stat cards, explicit task-type justification (multi-class classification, why not regression/ranking) - research-questions (after problem-statement): two-column literature gap layout + RQ1/RQ2/RQ3 - solution-overview (after research-questions): inline-SVG block diagram of the pipeline (fleet hosts → receiver → episodes → windowing → model zoo → per-window phase → trust score → containment + reset) - evaluation-setup (between chunking and models): four blocks covering split recipe, primary metric, baselines compared, and what's reported alongside accuracy. Each block leads with the *why*, matching the assignment's "explain not only what will be measured but why" requirement. - conclusion-future (before references): two-column "what we showed" + unsupervised next steps (clustering / anomaly / SSL pretrain / embedding viz). Addresses Section 8 of the assignment guide. OPTIONAL - theoretical-contributions: window-centre labelling, schema-hashed checkpoints, cross-host as eval axis - practical-contributions: /proc-only deployment, producer-agnostic dashboard, labelled dataset on disk - design-principles: one-loop-many-models, typed events as contract, two-agent path ownership - limitations: two-host fleet, synthetic profiles, 10 Hz floor, KNN cross-host gap Plus references/links.md gains four real online references (PyTorch, XGBoost, scikit-learn, proc(5)) bringing the citation count from 8 to 12 — over the assignment's 10-source minimum. CSS additions cover the new layouts (.problem-claim, .problem-stats, .research-grid, .pipeline-svg + .pipeline-stage / .pipeline-arrow, .eval-blocks, .conclusion-grid). Limitations cards reuse the motivation-card pattern with an armed-phase amber marker for the "warning" feel. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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1,007 B
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10 lines
1,007 B
Markdown
# Reference Links
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- https://github.com/mitre/caldera?tab=security-ov-file — adversary emulation framework (red-team/blue-team)
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- https://github.com/PiyushxJangid/DLHIDS — deep-learning HIDS reference implementation
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- https://github.com/ArpanDFrank/Host-Intrusion-Detection-System-using-Hybrid-CNN-LSTM-Models-and-RL-Actor-Critic-Models — Hybrid CNN-LSTM + RL HIDS prior art
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- https://ieeexplore.ieee.org/document/9881803 — per-device trust establishment from network behaviour (cited on motivation scene)
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- https://pytorch.org/docs/stable/index.html — PyTorch reference (LSTM / GRU / CNN / Transformer module APIs used by the model zoo)
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- https://xgboost.readthedocs.io/en/stable/ — XGBoost reference (gradient-boosted-trees baseline; Chen & Guestrin, KDD 2016)
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- https://scikit-learn.org/stable/ — scikit-learn reference (KNN, KMeans, PCA, evaluation metrics)
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- https://man7.org/linux/man-pages/man5/proc.5.html — proc(5) — the Linux kernel interface this project's telemetry comes from
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