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arXiv Machine Learning By Logan Andrew North, Priya Sanjay Kaluskar, Shasi Kumar Ramachandran Prabhu, Peilong Li, Suman Saha

Adversarial RL for Port-Scan Evasion: Attacker Feature Visibility in Edge-Deployed IDS

Read the original on arXiv Machine Learning →

The paper studies how an adaptive attacker can evade machine‑learning intrusion detection systems (IDS) deployed on a Raspberry Pi 3B+ by using a Deep Q‑Network (DQN) to learn port‑scan tactics that vary probe timing, TCP flags, and payload size. Three IDS models—XGBoost, a multi‑layer perceptron, and a 1D convolutional neural network—trained on TON_IoT telemetry were evaluated under black‑box, gray‑box, and white‑box feature‑visibility scenarios. Evasion success rates ranged from 61.9 % to 98.3 %, showing that even limited feature knowledge can enable high‑rate evasion and that the impact of feature visibility depends on the specific IDS model.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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