Graph-Based Machine Learning Techniques for Scalable Network Analysis and Anomaly Detection in Dynamic Cybersecurity Frameworks
Keywords:
Graph-based Machine Learning, Cybersecurity, Anomaly Detection, Network Analysis, Graph Neural Networks, Scalable Security SystemsAbstract
The increasing complexity of cyber threats necessitates the development of scalable, efficient, and robust cybersecurity frameworks. Graph-based machine learning (GBML) techniques have emerged as powerful tools for network analysis and anomaly detection, leveraging graph structures to model intricate relationships between network entities. This paper explores the role of GBML in scalable network analysis, reviews existing methodologies before 2023, and discusses their effectiveness in dynamic cybersecurity frameworks. We also present experimental results demonstrating the efficacy of these methods in real-world cybersecurity scenarios, supplemented with comparative analyses and performance metrics.
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