Challenges and Future Directions in Anomaly Detection

Abstract

Anomaly detection plays a critical role in various domains, including cybersecurity, finance, healthcare, and industrial monitoring by identifying unusual patterns or events that deviate from normal behavior. This chapter examines the challenges and future directions in anomaly detection, focusing on innovative techniques, emerging trends, and practical applications. Key challenges include the detection of subtle and evolving anomalies in large-scale, high-dimensional data streams, the integration of contextual information and domain knowledge for improved detection accuracy, and the mitigation of false positives and false negatives. Future directions encompass advancements in machine learning algorithms, such as deep learning and reinforcement learning, for enhanced anomaly detection performance, the integration of heterogeneous data sources and multi-modal information for comprehensive anomaly assessment, and the development of adaptive and self-learning anomaly detection systems capable of adapting to dynamic environments and evolving threats.