A systematic review of machine learning and deep learning techniques for anomaly detection in data mining - Tahir, Mahjabeen; Abdullah, Azizol; Izura Udzir, Nur; Azhar Kasmiran, Khairul (Taylor and Francis Ltd., 2025)
The growing use of the internet has increased the threat of cyberattacks. Anomaly detection systems are vital for protecting networks by spotting irregular activities. Various studies investigated anomaly detection techniques without a systematic approach. So far, the existing reviews mainly concerned time series and data streaming methods almost neglected the growing interest in graph-based data mining techniques which are vital in social networks, finance, and IoT domains. Following PRISMA guidelines, this systematic review examines anomaly detection methods applied to time series, data streaming, and graph-based data from 2018 to 2023. A total of 34 papers were selected from four key databases ScienceDirect, Scopus, Web of Science, and IEEE. In addition, this review addressed several issues with existing techniques including low scalability, explainability, and interpretability for real-time anomaly detection systems. In modern applications where data structures are complex, and processing requirements are high these traditional techniques are insufficient for real-time data processing. Finally, our study demanded more advanced, complex methods to address these evolving challenges.