Beyond Gradient-Based Attacks: Adversarial Robustness and Explainability Stability in Cybersecurity Classifiers
arXiv:2607.01679v1 Announce Type: cross Abstract: Adversarial attacks on cybersecurity classifiers pose a dual threat: degrading predictions and destabilising the SHAP-based explanations that security analysts rely on to understand and triage alerts. We extend our prior MLP conference study to Random Forest and XGBoost across four tabular security datasets (phishing URLs, UNSW-NB15, NF-ToN-IoT, HIKARI-2021), evaluating five attacks including three black-box methods applicable to non-differentiable tree models. We introduce the Explainability Stability Index (ESI), a scalar metric computed from
The proliferation of AI in cybersecurity necessitates robust attack and explanation stability evaluations to maintain trust and effectiveness of defensive systems.
Sophisticated readers should care about this as it directly addresses the vulnerabilities of AI-powered cybersecurity, which is crucial for national and corporate security.
The introduction of the Explainability Stability Index and the evaluation of black-box attacks on tree models provide new tools and insights for developing more resilient and transparent AI in cybersecurity.
- · Cybersecurity AI developers
- · Organizations using AI for security
- · Security analysts
- · AI robustness researchers
- · AI systems vulnerable to adversarial attacks
- · Organizations with immature AI security postures
- · Attackers relying on exploiting AI model weaknesses
Improved adversarial robustness and explainability stability in AI-driven cybersecurity systems.
Increased adoption of these robust AI systems, leading to more resilient cyber defenses across various sectors.
A potential arms race between robust AI defenses and more sophisticated adversarial AI attacks, driving continuous innovation in both fields.
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Read at arXiv cs.LG