CyberHull: Deterministic Malicious URL Isolation via Convex Hull
CyberHull replaces probabilistic ML for phishing-URL detection with a deterministic convex-hull approach, potentially reducing evasion rates and false positives — worth evaluating for SOC teams drowning in misclassified URL alerts.
Summary written by editorial AI · Source link below
arXiv:2610.27675v1 Announce Type: new Abstract: The emergence of increasingly sophisticated and evasion-resistant phishing attack strategies represents a significant threat to modern cybersecurity, particularly in cyber-physical systems and internet-based networks. Traditional probabilistic methods and certain machine learning approaches often struggle to detect novel attacks and may lack interpretability or portability. To address these challenges, we introduce CyberHull, a resource-efficient
Editorial Analysis
High false-positive rates in URL classification waste SOC analyst time; a deterministic geometric approach could improve precision and resist adversarial evasion techniques.
Benchmark CyberHull against your current URL-filtering stack on recent phishing campaigns to assess real-world improvement.
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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