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CyberHull: Deterministic Malicious URL Isolation via Convex Hull

Avijit Gayen, Aniket Ghosal, Durba Chakraborty, Sayan Mondal, Md Abdul Aziz Al Aman, Angshuman Jana · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27675 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

ますます洗練され、回避に抵抗するフィッシング攻撃戦略の出現は、特にサイバー物理システムやインターネットベースのネットワークにおいて、現代のサイバーセキュリティに重大な脅威をもたらします。伝統的な確率主義的方法と特定の機械学習アプローチは、新しい攻撃を検出するのに苦労し、解釈性や携帯性が欠けている可能性があります。これらの課題に対処するために、我々は、数学的に定義された複数の正方形の機能ドメイン、構造的なコンテンツエントロピー、ハイパーリンクグラフトポロジー、およびセマンティック・トークン分布を含む複雑な穴の境界を介して悪意のあるURLを識別するためのリソース効率的および

Machine translation (Meta M2M100); the English original follows.

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 and deterministic framework for identifying malicious URLs through mathematically defined convex hull boundaries across multiple orthogonal feature domains, including structural content entropy, hyperlink graph topology, and semantic token distribution. CyberHull constructs compact convex hulls, referred to as vaults, using the QuickHull algorithm, with a training complexity of O(n log n) under suitable assumptions. URL classification is performed through point-in-polyhedron tests that impose relatively low computational overhead on standard CPUs. Experimental results demonstrate that CyberHull achieves 92 percent detection accuracy, substantially outperforming a baseline linear approach based on Principal Component Analysis (PCA), which achieves 32 percent accuracy. Furthermore, CyberHull offers a computationally efficient alternative to resource-intensive deep learning approaches for malicious URL detection.

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Topics: Computer science, Interpretability, Probabilistic logic, Convex hull, Overhead (engineering), Theoretical computer science

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