Vol. 8, Special Issue 3 (2019)

Author(s):
Dr. Yogesh Bhomia, Sheo Sahu and SP Singh

Abstract:
Machine learning (ML) has revolutionized anomaly detection, offering powerful tools to identify deviations from established patterns in diverse domains. This review paper comprehensively explores the landscape of ML-based anomaly detection approaches, delving into their strengths, limitations, and suitability for various applications. We begin by outlining the fundamental concepts of anomaly detection and its significance in real-world scenarios. Subsequently, we delve into the major categories of ML approaches employed for anomaly detection, including supervised, unsupervised, and semi-supervised techniques. Each category is explored in detail, highlighting its underlying principles, representative algorithms, and practical considerations. We then delve into the critical challenges that impede the efficacy of anomaly detection systems, encompassing data quality issues, imbalanced class distributions, concept drift, and the curse of dimensionality. To bridge the gap between theory and practice, we showcase the diverse applications of ML-powered anomaly detection across various sectors, including fraud prevention, cybersecurity, network intrusion detection, healthcare diagnostics, and industrial predictive maintenance. Finally, we conclude by discussing emerging trends and future directions in the field, emphasizing the potential of novel techniques like deep learning and reinforcement learning to further enhance anomaly detection capabilities.