TRACE is presented, a framework that maps calibrated detector outputs to a finite action ladder, constructs conformal action sets, selects actions via a utility-minimax rule, and releases high-severity actions only when compact support contracts remain stable under feasible perturbations.
Abstract
Explainable intrusion detection systems often provide feature attributions without indicating whether a security action should be released, downgraded, or deferred. This paper investigates whether action-governed explanations can provide bounded triage evidence under traffic feature feasibility constraints. We present TRACE, a framework that maps calibrated detector outputs to a finite action ladder, constructs conformal action sets, selects actions via a utility-minimax rule, and releases high-severity actions only when compact support contracts remain stable under feasible perturbations, where feasibility is a property of the processed benchmark features and not of packet-level realizability. Ablations isolate the conformal set and release gate as the primary drivers of system behavior. Across 11 gated dataset–model pairs, TRACE produces non-degenerate action sets with zero full-set collapse and defer/block rates from 0.603 to 1.000. Under held-out sample split tuning, it achieves higher average proxy utility than unconditional release and release rate-matched random release on all 11 pairs. Against the strongest simple selective gate, however, it matches on 6 of 11 pairs and trails on the remaining 5. Robustness sweeps confirm positive all-row utility on all pairs, though pass-only utility becomes fragile in ultra-low-release regimes. Unlike display-only attribution summaries, the TRACE contract records the plausible action set, feasibility checks, stability summaries, and an explicit release rationale. The results support TRACE as a bounded evidentiary framework for action-governed XAI in IDS, rather than claiming superiority over all IDS/XAI methods or general deployment readiness.
Multi-turn attacks on agentic systems can compose individually permissible actions into harmful outcomes, challenging defenses that assess actions or states in isolation. We show that such attacks leave a detectable signature in the agent's internal representations: harmful behavior emerges as an accumulated representa...
Hao-Yu Wang, Wei Zhao, Ye-Di Zhang et al.· 1 citation
The result is a bounded rule for interpreting aggregate agent behavior: first establish exposure, then score change, and abstain when the trace cannot support the claim.
Privacy exposure displacement, the mismatch between a local evaluation proxy and target-grounded session exposure, and ASLEval, an authorization-aware framework that pre-registers a hidden target set, measures all declared visible exits, and reserves internal traces for diagnosis are introduced.
Guo-Xin Wu, Hui-Zhen Huang, Guo-Xiong Long et al.· 0 citations
Random Forest and other tree-ensemble classifiers achieve high accuracy in network intrusion detection; however, their aggregate decision logic prevents analysts from auditing or deploying individual predictions as operational rules. Post hoc explanation methods introduce latencies incompatible with security operation...
Saloua Bellouch, Mostapha Zbakh, S. Aouad et al.· Future Internet· 0 citations
Large language model (LLM) agents could help security operations centers (SOCs) investigate advanced persistent threats (APTs) by turning weak leads into evidence for intrusion scoping and response. Yet success under one telemetry setting does not establish robustness to changes in log collection, retention, or samplin...
This work defines behavioral safety and introduces ActBench, a self-evolving benchmark that evaluates such behavior risk from execution trajectories rather than final responses, and proposes a dual evidence verification mechanism that verifies agent execution safety and utility through log evidence and LLM-based trajec...
H. Yao, Yimin Liu, Meihui Chen et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.