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AI-Powered SOC: Anomaly Detection & Automated Incident Response (2D)

2D AI-SOC traffic-scoring lab: packets stream from network nodes toward a detection ring, an anomaly scorer drawn from two overlapping Gaussian distributions flags them against a tunable threshold, and adversarial evasion, auto-response and false-positive trade-offs play out live with a running detection-rate readout.

Cybersecurity2DModerate60 FPS📱 Mobile-adapted⇄ 3D version

Details

CategoryCybersecurity

RenderingCanvas 2D

DifficultyModerate

Target FPS60

ControlsMalicious-traffic and detection-threshold sliders, packet spawn-rate slider, Auto-response and Adversarial evasion toggles, and a Reset counters button; watch the live detected/false-positive/breach/detection-rate readout respond

Canvas 2DAnomaly DetectionSOCAdversarial MLIncident Response

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This 2D companion drives the same anomaly-scoring model as the 3D version on a flat ring layout: packets fly from network nodes toward a central AI SOC core, and the moment a packet crosses the cyan detection ring it is scored against two overlapping Gaussian distributions — N(0.18, 0.07) for benign traffic and N(0.68, 0.15) for malicious traffic — and flagged if the score clears the threshold τ. Auto-response quarantines a flagged packet right at the ring; adversarial evasion shaves a fixed budget off a malicious packet's score first, letting it slip under the threshold the same way an FGSM-style perturbation fools a real ML-based intrusion detector. A live readout tracks detections, false positives, breaches and the running detection rate as you retune the threshold, attack mix and spawn rate.

⚙ Under the hood

2D AI-SOC traffic-scoring lab: packets stream toward a detection ring, a Gaussian anomaly scorer flags them against a tunable threshold, and adversarial evasion, auto-response and false-positive trade-offs play out live.

Canvas 2DAnomaly DetectionSOCAdversarial MLIncident Response

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install

Why does raising the threshold increase breaches?

A higher τ means fewer false positives, but some malicious packets whose scores land just above the benign distribution's tail no longer clear the bar, so more of them slip through unflagged and count as breaches when they reach the core.

What does "Auto-response: OFF" actually change?

A flagged packet still gets flagged and colored, but nothing physically stops it — it keeps flying to the core. If it's malicious, it still counts as a breach even though the SOC raised an alert, modeling the gap between detection and actual containment.

How does adversarial evasion work here?

When ON, every malicious packet's anomaly score is reduced by a fixed 0.30 budget (floored at 0.03) before it's compared against the threshold — the same idea as an attacker perturbing a real input to push it just under an ML classifier's decision boundary.