Authors : Musa Tanimu Karatu; Ibrahim Musa Mungadi; Anas Shehu

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/5fvmvmyt

Scribd : https://tinyurl.com/5bbmbk4z

DOI : https://doi.org/10.38124/ijisrt/26mar1639

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The increasing complexity and prevalence of cyber threats have made the adoption of intelligent and adaptive security frameworks imperative. Traditional signature-based detection methods have proven insufficient, particularly in identifying zero-day exploits and rapidly evolving attack patterns. In this context, machine learning (ML) has emerged as a robust approach to cybersecurity threat detection, owing to its ability to learn underlying patterns and detect anomalies within large and complex datasets. This study presents a comprehensive review of machine learning techniques applied in cybersecurity, encompassing supervised, unsupervised, and deep learning paradigms. It examines the application of these techniques in key areas such as intrusion detection, malware analysis, phishing detection, and network traffic monitoring. Furthermore, the paper evaluates commonly utilized datasets, performance metrics, prevailing challenges, and prospective research directions. The findings reveal that hybrid and deep learning models generally outperform conventional methods in terms of detection accuracy and adaptability. However, challenges such as data imbalance and vulnerability to adversarial attacks continue to pose significant limitations, highlighting the need for further research and innovation in this domain.

Keywords : Cyber Security, Detection, Learning, Machine, Techniques, Threat.

References :

1. Ahmad, I., Basheri, M., Iqbal, A., & Rahim, N. (2021). Performance comparison of support vector machine and random forest for intrusion detection systems. International Journal of Advanced Computer Science and Applications, 12(2), 1–10.
2. Buczak, A. L., & Guven, E. (2020). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153–1176.
3. Diro, A. A., & Chilamkurti, N. (2018). Distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Computer Systems, 82, 761–768.
4. Dua, S., & Du, X. (2016). Data mining and machine learning in cybersecurity. CRC Press.
5. Ferrag, M. A., & Maglaras, L. (2021). Cyber security and machine learning: A systematic review. Computer Science Review, 42, 100–110.
6. Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2022). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Network and Computer Applications, 172, 102–140. https://doi.org/10.1016/j.jnca.2020.102823
7. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
8. Khan, W. Z., et al. (2021). Machine learning and deep learning approaches for intrusion detection systems: A review. IEEE Access, 9, 1–20.
9. Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification techniques. Informatica, 31, 249–268.
10. Liu, H., Lang, B., & Li, M. (2021). Machine learning-based malware detection: A survey. ACM Computing Surveys, 54(6), 1–36. https://doi.org/10.1145/3460458
11. Mitchell, T. M. (1997). Machine learning. McGraw-Hill.
12. Moustafa, N., & Slay, J. (2015). UNSW-NB15: A comprehensive data set for network intrusion detection systems. Military Communications and Information Systems Conference (MilCIS).
13. Nguyen, T. T., & Armitage, G. (2021). A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys & Tutorials, 10(4), 56–76.
14. Sahu, A., & Shrivastava, V. (2020). Network traffic analysis using machine learning: A review. Procedia Computer Science, 167, 194–203. https://doi.org/10.1016/j.procs.2020.03.200
15. Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. In Proceedings of the International Conference on Information Systems Security and Privacy (ICISSP).
16. Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A deep learning approach to network intrusion detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 41–50. https://doi.org/10.1109/TETCI.2017.2772792
17. Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. In Proceedings of the IEEE Symposium on Security and Privacy, 305–316.
18. Vinayakumar, R., Soman, K. P., & Poornachandran, P. (2020). Deep learning approach for intelligent intrusion detection system. IEEE Transactions on Network and Service Management, 17(2), 1–12.
19. Zhang, J., Chen, Z., & Zhao, X. (2022). Adversarial machine learning in cybersecurity: A survey. IEEE Transactions on Dependable and Secure Computing, 19(4), 1–18.
20. Zhou, Z. H. (2021). Ensemble methods: Foundations and algorithms. CRC Press.