Comparing Hyperbolic Graph Embedding models on Anomaly Detection for Cybersecurity
Authors: 
Mohamed Yacine Touahria Miliani, Souhail Abdelmouaiz Sadat, 
Mohammed Haddad, Hamida Seba, Karima AmroucheAuthors Info & Claims
Mohamed Yacine Touahria Miliani, Souhail Abdelmouaiz Sadat, Mohammed Haddad, Hamida Seba, Karima AmroucheAuthors Info & ClaimsARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security
Article No.: 118, Pages 1 - 11
Abstract
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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ARES 2024: The 19th International Conference on Availability, Reliability and Security
July 30 - August 2, 2024
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