Explaining Intrusion Alert Decisions of Deep Learning-based Network Intrusion Detection Systems for Security Analysts
EXP-SEC translates deep-learning NIDS alerts into analyst-readable explanations aligned with SOC workflows — addressing the chronic false-positive fatigue problem.
Summary written by editorial AI · Source link below
arXiv:2607.12203v1 Announce Type: new Abstract: In this paper, we present EXP-SEC, a novel framework which can explain the intrusion detection decisions of DL-based NIDS (which lead to security alerts) in a way that is aligned with the domain knowledge of analysts working in Security Operations Center (SOC). We highlight the following features of our framework: (1) a forensic module that isolates the suspect packets/flow which likely caused an alert (2) an explanation module which can handle mu
Editorial Analysis
Explainable NIDS alerts could significantly reduce SOC analyst burnout and accelerate triage, directly impacting mean-time-to-respond.
Evaluate explainable-AI add-ons for your NIDS stack to determine if they improve analyst decision quality and triage speed.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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