The Effectiveness of Machine Learning Techniques in Anomaly Detection for Cyberattack Prevention: Systematic Literature Review 2020-2025

Arie Budiansyah; Zulfan Zulfan; Nizamuddin Nizamuddin; Rudi Arif Candra; Dirja Nur Ilham; Nazaruddin Nazaruddin

doi:10.47709/brilliance.v5i1.6124

The Effectiveness of Machine Learning Techniques in Anomaly Detection for Cyberattack Prevention: Systematic Literature Review 2020-2025

Authors

Arie Budiansyah Universitas Syiah Kuala, Indonesia
Zulfan Universitas Syiah Kuala, Indonesia
Nizamuddin Universitas Syiah Kuala, Indonesia
Rudi Arif Candra Politeknik Aceh Selatan, Indonesia
Dirja Nur Ilham Politeknik Aceh Selatan, Indonesia
Nazaruddin Universitas Syiah Kuala, Indonesia

DOI:

https://doi.org/10.47709/brilliance.v5i1.6124

Keywords:

Machine Learning, Anomaly Detection, Cybersecurity, Cyberattacks, Systematic Literature Review

Abstract

As digital technology evolves, cyberattacks are becoming more diverse and difficult to detect. Conventional detection methods are often incapable of recognizing new and sophisticated attack patterns. Therefore, machine learning techniques are starting to be widely used because of their ability to study data patterns and detect unusual or anomalous activities. This study aims to systematically examine the effectiveness of various machine learning techniques in detecting anomalies as an effort to prevent cyberattacks. The research was conducted using the Systematic Literature Review (SLR) method on 20 scientific articles from reputable journals published between 2020 and 2025. The articles were selected through a search, selection, and analysis process following PRISMA guidelines. The results of the study show that algorithms such as Random Forest and Decision Tree consistently provide accurate detection results, especially in network systems and the Internet of Things (IoT). Meanwhile, deep learning techniques such as CNN and LSTM show high performance in handling large and complex data. However, challenges are still found in terms of data imbalances, high computing requirements, and lack of model interpretability. The conclusions of this study show that machine learning techniques are very promising for anomaly detection in cybersecurity, but an adaptive and easy-to-explain approach is needed. Researchers are further advised to develop models that are more efficient, transparent, and able to adapt to evolving cyber threats.

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Published

2025-06-25

How to Cite

Budiansyah, A., Zulfan, Z., Nizamuddin, N., Candra, R. A., Ilham, D. N., & Nazaruddin, N. (2025). The Effectiveness of Machine Learning Techniques in Anomaly Detection for Cyberattack Prevention: Systematic Literature Review 2020-2025. Brilliance: Research of Artificial Intelligence, 5(1), 259–271. https://doi.org/10.47709/brilliance.v5i1.6124