Navigating Anomaly Detection in Complex Networks: Methods, Challenges, and Recent Advances

The paper provides a comprehensive examination of the strategies and challenges in identifying anomalies within complex networks, which are crucial for maintaining the security and reliability of systems like social media, telecommunications, and financial networks. It classifies anomalies into different types—node, edge, subgraph, and temporal—each requiring specific detection methods. The paper reviews a broad spectrum of anomaly detection techniques, from traditional statistical methods to advanced machine learning and graph-based approaches. Recent advancements, particularly in deep learning with Graph Neural Networks (GNNs) and autoencoders, have significantly enhanced the ability to detect anomalies in dynamic and high-dimensional networks. Privacy-preserving approaches like federated learning are also discussed, highlighting the growing need to secure data while maintaining detection efficiency. Challenges such as scalability, the evolving nature of networks, high-dimensional data, and the interpretability of detection models are critically analyzed. The paper advocates for future research focused on developing more adaptive, interpretable, and benchmarked models to handle the complexities of modern networks. Practical applications are demonstrated through case studies, emphasizing the vital role of anomaly detection in real-world network management.