Enhancing Network Security through Machine Learning-Based Anomaly Detection Systems


  • Salam Allawi Hussein, Sándor R. Répás


Machine Learning, Anomaly Detection, Network Security, Data privacy and protection.


For decades, anomaly detection has been used to discover and extract aberrant components from data. Several techniques have been employed to spot irregularities. Machine learning (ML) is a method that is gaining importance due to its significant significance in this area. Machine learning models that detect anomalies in their application are the focus of this study's Systematic Literature Review (SLR). In our investigation, we look at the models from four angles: how anomaly detection is classified, what it's used for, how machine learning is done, and how well machine learning models perform. In this study, we looked for papers published in 2015–2023, which deal with the topic of anomaly detection using machine learning techniques. After we've finished analyzing the selected research papers, we'll go on to outline 10 different uses of anomaly detection that were found in those publications. The number of machine learning models used to detect anomalies is also identified, accounting for 6% of all instances. Finally, we offer available a wide range of datasets used in anomaly detection studies as well as many other generic datasets. Furthermore, compared to other categorized anomaly detection methods, researchers are more likely to employ unsupervised anomaly detection. The application of machine learning models for anomaly detection is one of the most promising fields of study, and researchers have utilized several ML models in this regard. Therefore, based on the results of this review, we advise and suggest things to researchers.


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How to Cite

Salam Allawi Hussein. (2024). Enhancing Network Security through Machine Learning-Based Anomaly Detection Systems. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1929–1935. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5762



Research Article