XAI-SDN-IDS

Explainable intrusion detection with confidence-gated response
Connecting… Ryu controller Mininet testbed – switches
Automatic protection

Decision pipeline

Waiting for the first 2-second window…

1 · Flow counters

What the switches reported for this host in the last window.

2 · Features

14 values computed from the counters. Bold = top SHAP drivers.

3 · Random Forest

Share of 150 trees voting for each class.

Ticks: act at 0.70 · block at 0.90

4 · SHAP explanation

Why the model chose this class.

supportsargues against

5 · Safety gates

6 · Decision

Network

Select a host to inspect it. ↑ sent ↓ received in packets/s. Moving dashes = active traffic.

Packets sent per host

Up to the last 10 minutes · log scale · shaded = launched episode · red line = mitigation

Decision log

Every window the model classified as an attack, with its explanation and gate results
TimeHostPredictionWhy (top SHAP features)GatesAction
No attack-like traffic yet. Launch an attack from Controls to see the pipeline react.

Active mitigations

Rules expire on their own

Controls

Watching is open to everyone. Sign in to launch attacks or change protection.

Launched episodes

Measured live: time from launch to first detection and to mitigation
StartEpisodeDetectedMitigatedResult
Nothing launched yet.

Offline evaluation

Trained on session train1, tested on the independent session test2

Macro averages over 5 classes. FPR and FNR treat the task as benign vs attack. Random Forest is deployed.

Random Forest confusion matrix on the held-out session

Gating ablation

Replay of the held-out session through each policy

Attack sources (h4, h7) are excluded from "innocent hosts". Naive blocking cut off flood victims whose replies looked like floods. Gating removed every innocent-host block while stopping all 12 attacks.

Per-class results

What drives each attack class

Mean |SHAP| on the held-out session; these confirm the gate G3 signatures
Top SHAP features per attack class

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