Unsupervised Machine Learning for Anomaly Detection: A Systematic Review

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 7

Year of Publication : 2025

Authors : Mohammad Nazmul Alam, Vijay Laxmi, Narender Kumar, Reetu Kumari, Sahil Sharma

10.14445/23488379/IJEEE-V12I7P104

How to Cite?

Mohammad Nazmul Alam, Vijay Laxmi, Narender Kumar, Reetu Kumari, Sahil Sharma, "Unsupervised Machine Learning for Anomaly Detection: A Systematic Review," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 7, pp. 34-72, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I7P104

Abstract:

Anomaly detection has long been employed to consider and isolate abnormal components in data, with a variety of techniques developed for this purpose. One increasingly prominent approach is Machine Learning (ML), which has become instrumental in this field. In this article, we present a systematic literature review converging on anomaly detection using unsupervised machine learning algorithms. Our review examines anomaly detection models through three key dimensions: the applications of anomaly detection, the Unsupervised Machine Learning (UnML) techniques used, and the performance metrics for UnML models. We reviewed 169 research articles published between 2016 and 2024, all of which explore UnML techniques for anomaly detection. From this pool, 116 papers were selected for detailed analysis. Our review identified 58 distinct applications of anomaly detection and 34 unique UnML models employed across these studies. The frequency of various techniques highlights their application in anomaly detection and data processing. Autoencoder is the most frequently used technique, with 12 mentions. Isolation Forest follows with 5 times, while LSTM+Autoencoder appears 4 times. Methods such as IF+AE, LOF, COF, and k-Means are used twice. Hidden Markov Model, Random Histogram Forest, AutoGAN, DBSCAN, CNN+BiLSTM, DeepAE+CNN, Small Recurrent+CNN, PCA, GAN, CNN, LSTM, Autoencoder+Clustering, Hybrid CNN, COF, HBOS, OCSVM, SLOF, LDF, ORCA, LSTM+GAN, OCRF, OCSUM, OCCNN, OCNN, CVAE, C-Means, Entropy, and DAE+EIF are each mentioned once, showing a diverse range of techniques applied in the field. Notably, our findings highlight that the integration of heterogeneous methods is a promising avenue for future research. These advanced techniques offer substantial potential for enhancing the precision and effectiveness of anomaly detection in unsupervised machine learning contexts.

Keywords:

Systematic review, Unsupervised machine learning, Anomaly detection, Accuracy, Evaluation.

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