Comparing Hyperbolic Graph Embedding models on Anomaly Detection for Cybersecurity - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

Comparing Hyperbolic Graph Embedding models on Anomaly Detection for Cybersecurity

Résumé

Graph-based anomaly detection has emerged as a powerful tool in cybersecurity for identifying malicious activities within computer systems and networks. While existing approaches often rely on embedding graphs in Euclidean space, recent studies have suggested that hyperbolic space provides a more suitable geometry for capturing the inherent hierarchical and complex relationships present in graph data. In this paper, we explore the efficacy of hyperbolic graph embedding for anomaly detection in the context of cybersecurity. We conduct a comparison of six state-of-the-art hyperbolic graph embedding methods, evaluating their performance on a well-known intrusion detection dataset. Our analysis reveals the strengths and limitations of each method, demonstrating the potential of hyperbolic graph embedding for enhancing security.
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Dates et versions

hal-04688906 , version 1 (05-09-2024)

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Citer

Mohamed Yacine Touahria Miliani, Souhail Abdelmouaiz Sadat, Mohammed Haddad, Hamida Seba, Karima Amrouche. Comparing Hyperbolic Graph Embedding models on Anomaly Detection for Cybersecurity. ARES 2024: The 19th International Conference on Availability, Reliability and Security (GRASEC Workshop), Jul 2024, Vienna Austria, France. pp.1-11, ⟨10.1145/3664476.3670445⟩. ⟨hal-04688906⟩
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