Traffic-Aware Randomized Smoothing for LLM-Based Network Intrusion Detection
Large language model (LLM)-based intrusion detection systems (IDS) are increasingly studied for security monitoring, yet their robustness against feasible traffic manipulation remains largely empirical. We present Traffic-Aware Randomized Smoothing (TA-RS), a classifier-agnostic certified defense that injects Gaussian noise exclusively into the directly controllable (DC) subspace -- features a remote attacker can modify -- during both fine-tuning and certification, aligning the smoothing distribution with the attacker-controllable subspace. We identify a critical prerequisite: applying standar
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- LinkedLinked via arxiv author · 85%Zhenpeng Li →
“Traffic-Aware Randomized Smoothing for LLM-Based Network Intrusion Detection”
- PossiblePossibly related (embedding) · 46%Open-sourcing a two-stage prompt-injection detector (regex gate + quantised DeBERTa-v3 ONNX), trained partly on real attacks from a game I ran [P] →
- PossiblePossibly related (embedding) · 52%An LLM-based synthetic data generation approach for addressing class imbalance in malicious traffic detection - Nature →