Shallow Learning vs. Deep Learning in Anomaly Detection Applications

This chapter delves into the comparative analysis of shallow learning and deep learning techniques within the context of anomaly detection applications. Anomalies, deviations from normal patterns in data, pose significant challenges across various domains, necessitating effective detection mechanisms. Shallow learning methods, characterized by their simplicity and interpretability, have historically been employed for anomaly detection. However, recent advancements in deep learning have introduced complex, data-driven models capable of capturing intricate patterns and dependencies. In the field of anomaly detection, this chapter methodically examines and compares the benefits and drawbacks of shallow and deep learning techniques. We examine key methodologies, algorithmic frameworks, and empirical studies, shedding light on their performance, scalability, and interpretability. Moreover, we investigate hybrid strategies that take advantage of the complementing advantages of both paradigms. We are hopeful that this thorough analysis will help researchers, practitioners, and decision-makers navigate the complex field of anomaly detection by offering insightful guidance on which strategies should be utilized in light of particular application needs and data features.