Aug 2026· Journal of Military Science and Technology· 0 citations· 8 references
TL;DR
This paper proposes a structural-semantic integration framework leveraging Graph Attention Networks and Knowledge Distillation for robust multi-layer malicious URL detection, conceptualize raw URLs as character-level directed graphs to extract intricate structural patterns.
Abstract
Malicious URLs are evolving with increasingly sophisticated obfuscation techniques, posing a persistent threat to global cybersecurity. Traditional deep learning methods still struggle to identify the intricate topological characteristics and hidden structural dependencies inherent in URL sequences. This paper proposes a structural-semantic integration framework leveraging Graph Attention Networks and Knowledge Distillation for robust multi-layer malicious URL detection. We conceptualize raw URLs as character-level directed graphs to extract intricate structural patterns, fused with high-level lexical context distilled from a pretrained DistilBERT model. A hybrid loss integrating Kullback-Leibler divergence and Negative Log-Likelihood balances computational efficiency and predictive performance. Experimental results on large-scale, imbalanced datasets show our framework achieves 98.86% accuracy and 98.16% F1-score.
Experimental results on real-world datasets demonstrate that SHEAR significantly outperforms existing methods in fake news detection, showcasing the benefits of incorporating behavioral semantic patterns with network motifs for enhanced classification performance.
Shuo Yu, Yu-Peng Gao, Jing Ren et al.· IEEE Transactions on Neural...· 0 citations
Phishing attacks are increasingly prevalent, leading to the loss of assets, financial information, personal data, and other sensitive information to malicious actors. Various detection methods have been developed to protect users from such attacks. Traditional phishing Uniform Resource Locator (URL) detection methods,...
Shah Noor, S. Bazai, Muhammad Imran Ghafoor et al.· PeerJ Computer Science· 0 citations
Accurate network traffic classification is critical for defending against evolving cyber threats. However, mainstream methods relying solely on intra-flow features fail to distinguish malicious traffic with highly similar legitimate patterns, lacking contextual modeling and robustness to adversarial evasion. To address...
Peng Fang, Zi-Ming Zhao, Fei-Yang Huang et al.· IEEE Transactions on Informa...· 0 citations
Malicious website detection traditionally relies on URL analysis, but this is limited as attackers can create clean-looking links, abuse URL shorteners, or even hijack trusted websites. To address this gap, we shift the focus from analyzing technical artifacts to understanding intent, by proposing a content-aware appro...
Cuong Nguyen Viet Le, M. Hoang, Khuong Nguyen-Vinh· Discover Computing· 0 citations
Standard Graph Neural Networks (GNNs) frequently struggle with heterophilous structures where local proximity does not imply label similarity, leading to significant performance degradation due to signal oversmoothing. This study aimed to bridge the gap between theoretical complexity and empirical reliability by propos...
Heru Ismanto, I. Wayangkau· Engineering, Technology &...· 0 citations
Static graph anomaly detection is important in applications such as cybersecurity, fraud detection, and social network analysis. However, detecting anomalies in attributed graphs remains challenging because of complex graph structures and limited labeled data. Existing methods often focus on either structural informati...
Hessa O. Alharbi, Hedia Zardi· Communications of Internatio...· 0 citations
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