A Comprehensive Review on Graph-Based Anomaly Detection: Approaches for Intrusion Detection

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Abstract
Intrusion Detection Systems (IDSs) have evolved to safeguard networks and systems from cyber attacks. Anomaly-based Intrusion Detection Systems (A-IDS) have been commonly employed to detect known and unknown anomalies. However, conventional anomaly detection approaches encounter substantial challenges when dealing with complex, large-scale, and heterogeneous data sources. These challenges include high False Positive Rates (FPRs), imbalanced data behavior, complex data handling, resource constraints, limited interpretability, and difficulties with encrypted networks. This survey reviews Graph-based Anomaly Detection (GBAD) approaches, highlighting their ability to address these challenges by utilizing the inherent structure of graphs to capture and analyze network connectivity patterns. GBAD approaches offer flexibility for handling diverse data types, scalability to analyze large datasets, robustness detection capabilities, and enhanced interpretability through visualizations. We present a phased graph-based anomaly detection methodology for intrusion detection. This includes phases of data capturing, graph construction, graph pre-processing, anomaly detection, and post-detection analysis. Furthermore, we examine the evaluation methods and datasets employed in GBAD research and provide an analysis of the types of attacks identified by these methods. Lastly, we outline the key challenges and future directions that require significant research efforts in this area and offer some recommendations to address them.

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