Abstract:

Anomaly detection is an essential task across a variety of domains, including cybersecurity, finance, and industrial systems, where recognizing deviations from normal behavior is critical for preventing and mitigating potential threats. Traditional methods, such as statistical and machine learning techniques, have played a crucial role in laying the groundwork for anomaly detection. However, the emergence of deep learning has revolutionized the field, leading to the development of hybrid models that combine the strengths of multiple techniques. This literature review examines the evolution of anomaly detection methods, beginning with basic traditional methods and progressing to advanced hybrid deep learning models. It delves into statistical methods like Z-Score and Principal Component Analysis (PCA), which are effective for univariate and multivariate anomaly detection, respectively. The review highlights the advantages of deep learning in handling complex, high-dimensional data and discusses various hybrid models that integrate deep learning with traditional approaches. Case studies from different domains, such as network intrusion detection and financial fraud detection, are examined to illustrate the practical applications and benefits of these models. A comparative analysis of hybrid models is presented, evaluating their performance and discussing the challenges and future directions in the field. This comprehensive review emphasizes the importance of ongoing research to enhance the scalability, interpretability, and real-time capabilities of hybrid deep learning models for anomaly detection.

Description:

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