51f2437b71
The widget was waiting on live `phase` events that don't flow when no orchestrator is running, so it sat empty. Replace the rolling 5-minute window with a periodic feeder that samples 500 random episode tarballs from /var/lib/cis490/episodes, extracts each labels.jsonl, and aggregates phase durations using consecutive t_mono_ns deltas. Result lands in broadcaster.state["phase_mix"] (survives snapshot cycles via dict.update) and re-broadcasts every ~10 min. Frontend reads phase_mix from snapshot on connect and from live phase_mix events on refresh; the bar uses time-weighted proportions when available (falls back to label counts), and only sums canonical phases for the denominator so non-displayed `failed` records don't shrink the visible bars. Eyebrow and sub-line update with live sample/population/label counts. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
534 lines
24 KiB
HTML
534 lines
24 KiB
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<title>CIS490 — live</title>
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inside drift / lava / laser are generated by JS so the count
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and statistical-distribution sliders actually take effect. -->
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scanlines inside its area so they can't beat against the
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perspective floor — same shape, smaller scale). -->
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<div class="theme-panel-header">
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<span class="theme-title">theme · OKLCH</span>
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<button id="theme-close" class="ghost icon" title="Close (t)">×</button>
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<details class="theme-advanced">
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<summary>advanced — palette ladder</summary>
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<div class="theme-row">
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<span>palette</span>
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<details class="theme-advanced" open>
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<summary>animation · global</summary>
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<div class="theme-sliders">
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<label>speed · <span id="theme-speed-val">1.00</span>×
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<input type="range" id="theme-speed" min="0.1" max="4" value="1" step="0.05"></label>
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<input type="range" id="theme-tint" min="0" max="0.6" value="0.1" step="0.02"></label>
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<label>content backdrop · <span id="theme-backdrop-val">0.30</span>
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<input type="range" id="theme-backdrop" min="0" max="1" value="0.3" step="0.05"></label>
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<!-- Per-theme settings — dynamically built by JS from the THEMES
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spec; only the section matching state.background is shown. -->
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<div id="theme-bg-settings"></div>
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<div class="theme-meta-row">
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<code id="theme-meta">oklch(70% 0.15 250)</code>
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<button id="theme-reset" class="ghost">reset</button>
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</div>
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</div>
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<header class="topbar">
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<span class="brand">CIS490</span>
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<span id="status" class="status">connecting…</span>
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<span class="spacer"></span>
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<span class="counter"><span id="scene-idx">1</span> / <span id="scene-total">1</span></span>
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<button id="prev-btn" class="ghost icon" title="Previous (← / k)">◀</button>
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<button id="next-btn" class="ghost icon" title="Next (→ / space / j)">▶</button>
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<button id="click-nav-btn" class="ghost" title="Click on the stage to advance to the next slide (c)">click-nav: off</button>
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<button id="demo-btn" class="ghost" title="Toggle local synthetic data">demo: off</button>
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<button id="theme-btn" class="ghost" title="Theme panel (t)">theme</button>
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</header>
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<div class="layout">
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<div class="canvas-wrapper" id="stage-col">
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<div class="stage">
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<!-- 1. intro -->
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<div class="stage-view" data-view="intro">
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<div class="bg-grid"></div>
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<div class="intro-block">
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<div class="intro-eyebrow">cis490 · live fleet telemetry</div>
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<div class="intro-title">behavioral<br>malware<br>detection</div>
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</div>
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</div>
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<!-- 2. stack — Python stack & libraries used in the project -->
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<div class="stage-view" data-view="stack">
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<div class="metric-stack metric-stack-wide">
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<div class="metric-eyebrow">the stack behind the live data on the right</div>
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<div class="code-grid">
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<div class="code-card">
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<div class="code-card-header">pyproject.toml</div>
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<pre class="code" id="code-pyproject"></pre>
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</div>
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<div class="code-card">
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<div class="code-card-header">receiver/app.py · file header</div>
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<pre class="code" id="code-receiver"></pre>
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</div>
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</div>
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</div>
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</div>
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<!-- 3. collect -->
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<div class="stage-view" data-view="collect">
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<div class="metric-stack">
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<div class="metric-eyebrow">episodes ingested</div>
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<div class="metric-big" id="ingest-total">0</div>
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<div class="metric-sub">
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<span id="ingest-rate">0.0</span> / sec · last 60 s ·
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total bytes on disk: <span id="ingest-bytes">0 B</span>
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</div>
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<svg class="sparkline" id="ingest-spark" viewBox="0 0 600 120" preserveAspectRatio="none">
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<path id="ingest-spark-fill" d=""></path>
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<path id="ingest-spark-path" d=""></path>
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</svg>
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</div>
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</div>
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<div class="stage-view" data-view="hosts">
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<div class="metric-stack">
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<div class="metric-eyebrow">per-host shipping</div>
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<div class="bars" id="host-bars">
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<div class="awaiting">awaiting snapshot…</div>
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</div>
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</div>
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</div>
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<!-- 5. db — episode database explorer -->
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<div class="stage-view" data-view="db">
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<div class="metric-stack metric-stack-wide">
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<div class="db-header">
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<div class="metric-eyebrow">episode database · last 200 records</div>
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<div class="db-count" id="db-count">0 of 0</div>
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</div>
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<div class="db-controls">
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<div class="db-tabs" id="db-tabs"></div>
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<input class="db-search" id="db-search" type="text"
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placeholder="filter by host / id / sha…" />
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</div>
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<div class="db-table-wrap">
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<table class="db-table">
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<thead>
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<tr>
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<th>host</th>
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<th>episode_id</th>
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<th>received</th>
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<th>size</th>
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</tr>
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</thead>
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<tbody id="db-tbody"></tbody>
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</table>
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</div>
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<div class="db-detail" id="db-detail" hidden>
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<div class="db-detail-meta" id="db-detail-meta"></div>
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<div class="db-detail-chart-wrap">
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<svg class="db-detail-chart" id="db-detail-chart"
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viewBox="0 0 1000 360" preserveAspectRatio="none"></svg>
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</div>
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<div class="db-detail-legend" id="db-detail-legend"></div>
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</div>
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</div>
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</div>
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<!-- 6. baseline -->
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<div class="stage-view" data-view="baseline">
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<div class="metric-stack">
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<div class="metric-eyebrow" id="phase-mix-eyebrow">phase mix · sampling dataset…</div>
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<div class="phase-stack" id="phase-stack"></div>
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<div class="phase-legend" id="phase-legend"></div>
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<div class="metric-sub" id="phase-mix-sub">computing the phase
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distribution across a random sample of episodes on disk.
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A clean fleet sits mostly in <code>clean</code>; skew toward
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<code>infecting</code> / <code>infected_running</code>
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reflects time spent under attack workloads.</div>
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</div>
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</div>
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<!-- 7. attacks -->
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<div class="stage-view" data-view="attacks">
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<div class="metric-stack">
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<div class="metric-eyebrow">attack envelopes · /proc signature per profile</div>
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<div class="profile-grid" id="profile-grid"></div>
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</div>
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</div>
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<!-- 8. chunking -->
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<div class="stage-view" data-view="chunking">
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<div class="metric-stack">
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<div class="metric-eyebrow">10-second windows · model input shape</div>
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<div class="chunk-rule" id="chunk-rule"></div>
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<div class="chunk-row" id="chunk-row"></div>
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<div class="chunk-axis" id="chunk-axis"></div>
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<div class="metric-sub">each window: 100 samples (10 Hz × 10 s),
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labeled by the phase that occupies its center.</div>
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</div>
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</div>
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<!-- 9. models -->
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<div class="stage-view" data-view="models">
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<div class="metric-stack">
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<div class="metric-eyebrow">sequence models · accuracy on held-out samples</div>
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<div class="model-bars" id="model-bars"></div>
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</div>
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</div>
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<!-- 10. training-code — how we trained the sequence models -->
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<div class="stage-view" data-view="training-code">
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<div class="metric-stack metric-stack-wide">
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<div class="metric-eyebrow">how we trained the sequence models</div>
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<div class="code-card">
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<div class="code-card-header">training/models/lstm.py</div>
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<pre class="code" id="code-train-lstm"></pre>
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</div>
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</div>
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</div>
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<!-- 11. knn — interactive 3-D scatter with mode toggle -->
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<div class="stage-view" data-view="knn">
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<div class="metric-stack">
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<div class="metric-eyebrow">window features · 3-D projection · drag to rotate</div>
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<div class="scatter3d-controls">
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<div class="scatter3d-modes">
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<button class="scatter3d-mode active" data-mode="phase">phase (ground truth)</button>
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<button class="scatter3d-mode" data-mode="predicted">KNN-predicted label</button>
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<button class="scatter3d-mode" data-mode="cluster">cluster id</button>
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</div>
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<button class="scatter3d-reset">reset view</button>
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</div>
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<div class="scatter3d-wrap">
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<canvas class="scatter3d" id="knn-scatter-canvas"></canvas>
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</div>
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<div class="phase-legend" id="knn-legend"></div>
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</div>
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</div>
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<!-- 13. references — PDF viewer with tabs + description -->
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<div class="stage-view" data-view="references">
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<div class="metric-stack metric-stack-wide ref-stack">
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<div class="metric-eyebrow">references · papers, notes, prior work</div>
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<div class="ref-tabs" id="ref-tabs"></div>
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<div class="ref-content">
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<div class="ref-viewer-wrap">
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<iframe class="ref-viewer" id="ref-viewer"
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title="reference viewer"
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sandbox="allow-same-origin allow-scripts allow-popups allow-forms"></iframe>
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</div>
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<div class="ref-description" id="ref-description"></div>
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</div>
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</div>
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</div>
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<!-- 12. perf -->
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<div class="stage-view" data-view="perf">
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<div class="metric-stack">
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<div class="metric-eyebrow">accuracy vs inference cost</div>
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<svg class="scatter" id="perf-scatter" viewBox="0 0 600 360" preserveAspectRatio="xMidYMid meet"></svg>
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<div class="metric-sub">x: μs / window (lower is better) ·
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y: held-out accuracy (higher is better).</div>
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</div>
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</div>
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|
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</div>
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<button id="next-fab" class="fab" data-no-advance title="Next (→)">▼</button>
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</div>
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|
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<article class="article">
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<section class="scene" data-stage="intro">
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<div class="prose">
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<p class="lede">Most malware doesn't look like malware in a database
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— it looks like a process behaving badly.</p>
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<p>An <strong>intrusion detection system</strong> spots the bad
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behavior; an <strong>intrusion prevention system</strong> stops it.
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Both depend on knowing what bad behavior <em>looks like</em> at the
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level of telemetry the device can actually see.</p>
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<p>This deck is the live face of the dataset we're building to teach
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a model that distinction — every panel on the left is a slice of
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real data shipping in right now.</p>
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<p class="hint">scroll, click, or → to advance</p>
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</div>
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</section>
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<section class="scene" data-stage="stack">
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<div class="prose">
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<h2>Live, not staged</h2>
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<p>Every panel from here on is real data from real devices —
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counters, bars, the episode database, all driven by the
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<code>cis490-receiver</code> service running on this Pi as
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you scroll.</p>
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<p>The code on the left is how it gets here. Four runtime deps:
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<strong>starlette</strong> + <strong>uvicorn</strong> for the
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async HTTP and WebSocket surface, <strong>msgpack</strong>
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talks to Metasploit's RPC, <strong>pycdlib</strong> builds the
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lab-VM cidata ISOs. Everything else is the standard library,
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and every dep is annotated with a one-line reason it's there.</p>
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||
</div>
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||
</section>
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|
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<section class="scene" data-stage="collect">
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<div class="prose">
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<h2>Collecting the dataset</h2>
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<p>Each lab host on the WireGuard mesh boots a real Alpine VM, runs
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a profile-driven workload inside it, and samples
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<code>/proc/<qemu_pid></code> at 10 Hz. Every ~30 seconds
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the labeled tarball is shipped to this Pi over mTLS.</p>
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<p>The counter on the left is the running total, sourced from the
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receiver's <code>index.jsonl</code> on disk. The sparkline is the
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||
arrival rate over the last sixty seconds.</p>
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</div>
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</section>
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<section class="scene" data-stage="hosts">
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<div class="prose">
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<h2>A multi-host fleet</h2>
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<p>Running the same orchestrator on multiple hosts gives novel,
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non-overlapping data per host — no central coordinator. Each host
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pulls a different slice of the manifest, so the dataset grows in
|
||
parallel.</p>
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<p>The numbers below are absolute episode counts on disk, refreshed
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||
from <code>/var/lib/cis490/episodes/<host>/</code> every
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thirty seconds.</p>
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||
</div>
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||
</section>
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|
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<section class="scene" data-stage="db">
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||
<div class="prose">
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||
<h2>The dataset, browsable</h2>
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||
<p>Every row is one labeled episode tarball stored at
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||
<code>/var/lib/cis490/episodes/<host>/<id>.tar.zst</code>
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||
after the receiver verifies its SHA-256 and writes it through.</p>
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||
<p>Filter by host with the tabs, or grep by host / episode id /
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||
sha with the search box. Click a row for the full
|
||
<code>index.jsonl</code> record. The view holds the most recent
|
||
two hundred records — older history is on disk, indexable
|
||
from the receiver.</p>
|
||
</div>
|
||
</section>
|
||
|
||
<section class="scene" data-stage="baseline">
|
||
<div class="prose">
|
||
<h2>A baseline of normal</h2>
|
||
<p>Before we can detect a deviation, we have to know what the fleet
|
||
looks like when it's healthy. The stacked bar shows the fraction
|
||
of the last five minutes of fleet activity that sat in each phase
|
||
— a healthy mix has plenty of <code>clean</code>.</p>
|
||
<p>If the model only ever sees <code>clean</code>, it overfits to
|
||
"everything is fine." The phase schedule fixes that by forcing the
|
||
workload to walk through every phase on every run.</p>
|
||
</div>
|
||
</section>
|
||
|
||
<section class="scene" data-stage="attacks">
|
||
<div class="prose">
|
||
<h2>Linking attack to telemetry</h2>
|
||
<p>The same six profiles run across every host, and each one
|
||
produces a different envelope in <code>/proc</code>. A
|
||
cryptominer pegs one core for minutes. A bursty C2 channel sits
|
||
idle, then exhales three packets. Ransomware walks the
|
||
filesystem and saturates I/O.</p>
|
||
<p>The thumbnails on the left are the canonical envelopes the
|
||
model has to learn to recognize — same axes, different shapes.
|
||
That shape difference is what makes detection tractable.</p>
|
||
</div>
|
||
</section>
|
||
|
||
<section class="scene" data-stage="chunking">
|
||
<div class="prose">
|
||
<h2>Ten-second windows</h2>
|
||
<p>Models eat fixed-size inputs. We chop each episode into
|
||
10-second windows — 100 samples per window at 10 Hz — and
|
||
label each window with the phase that occupies its center.</p>
|
||
<p>Window size is a knob. Too short and the model can't see slow
|
||
envelopes (low-and-slow malware, idle C2). Too long and you can't
|
||
react fast enough to be a useful prevention signal. Ten seconds
|
||
is the starting point we tune around.</p>
|
||
</div>
|
||
</section>
|
||
|
||
<section class="scene" data-stage="models">
|
||
<div class="prose">
|
||
<h2>Sequence models</h2>
|
||
<p><strong>RNN, GRU, LSTM</strong> — recurrent models that read the
|
||
window one timestep at a time and carry state forward. Cheap,
|
||
mature, easy to interpret.</p>
|
||
<p><strong>BERT-style transformer</strong> — the window becomes a
|
||
sequence of "tokens"; attention captures cross-position context
|
||
instead of accumulating it through a hidden state. More
|
||
parameters, more compute, more room to overfit a small dataset.</p>
|
||
<p>Same input, same labels, four different inductive biases. The
|
||
comparison on the left is the punchline of the whole project.</p>
|
||
</div>
|
||
</section>
|
||
|
||
<section class="scene" data-stage="training-code">
|
||
<div class="prose">
|
||
<h2>How we trained them</h2>
|
||
<p>One trainer per model — load the windowed dataset, define the
|
||
network, train, evaluate. Same shape for RNN, GRU, LSTM, BERT,
|
||
so you can read all four side-by-side and the only differences
|
||
are the architecture itself.</p>
|
||
<p>The code on the left is the LSTM trainer.
|
||
PyTorch's <code>DataLoader</code> handles windowing,
|
||
<code>nn.LSTM</code> is one line, the loop is six.
|
||
No custom loss, no rate schedule, no manual batching —
|
||
anything fancier has to earn its place by beating the simple
|
||
version on held-out samples.</p>
|
||
</div>
|
||
</section>
|
||
|
||
<section class="scene" data-stage="knn">
|
||
<div class="prose">
|
||
<h2>Nearest-neighbor as a sanity check</h2>
|
||
<p>Before anything fancy: engineer summary features per window
|
||
(mean, std, p95, slope, zero-bucket counts per channel) and run
|
||
<strong>KNN</strong> in that feature space.</p>
|
||
<p>If the phase clusters separate visibly in two dimensions, KNN
|
||
already does most of the work and a deep model is only buying
|
||
marginal improvement. If they don't separate, you've learned
|
||
something about the feature engineering before training a single
|
||
epoch.</p>
|
||
</div>
|
||
</section>
|
||
|
||
<section class="scene" data-stage="perf">
|
||
<div class="prose">
|
||
<h2>Accuracy vs complexity</h2>
|
||
<p>Bigger models earn better numbers in the validation set — but
|
||
they also need more parameters, more inference time, and more
|
||
memory at the edge. The deployed model has to fit on the device
|
||
it's protecting.</p>
|
||
<p>The scatter on the left is the usable trade-off curve: every
|
||
point above and to the left of where you currently sit is a
|
||
reachable upgrade. The point in the bottom-right is a model
|
||
you'd never ship.</p>
|
||
</div>
|
||
</section>
|
||
|
||
<section class="scene" data-stage="references">
|
||
<div class="prose">
|
||
<h2>References</h2>
|
||
<p>The papers, notes, and prior work this project leans on.
|
||
Pick a tab on the left to load the document; the viewer
|
||
takes the bulk of the stage so you can scroll through
|
||
without leaving the deck.</p>
|
||
<p class="hint">end of deck · ← to flip back</p>
|
||
</div>
|
||
</section>
|
||
|
||
<div class="scene-end-spacer"></div>
|
||
</article>
|
||
</div>
|
||
|
||
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||
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|
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