A Review of Deep Belief Networks in Intrusion Detection Systems: Applications, Optimization Techniques, and Dataset Utilization

Sule Aishat A.; Alhassan John K.; Ismaila Idris; Alabi Isiaq O.; Subairu Sikiru O.

doi:10.56134/jst.v4i1.114

Authors

Sule Aishat A. Federal University of Technology
Alhassan John K. Federal University of Technology
Ismaila Idris Federal University of Technology
Alabi Isiaq O. Federal University of Technology
Subairu Sikiru O. Federal University of Technology

Keywords:

Deep Belief Networks (DBN), Intrusion Detection Systems (IDS), Cybersecurity, Optimization Techniques, Benchmark Datasets
doi

https://doi.org/10.56134/jst.v4i1.114

Abstract

As reliance on the Internet and interconnected systems for essential services continues to grow, the need for strong cybersecurity defenses has become more pressing. Intrusion Detection Systems (IDS) are crucial in safeguarding these digital infrastructures. This paper investigates how Deep Belief Networks (DBNs) can enhance IDS capabilities, particularly in identifying advanced and dynamic threats such as Distributed Denial of Service (DDoS) attacks, SQL injections, and zero-day vulnerabilities. By reviewing recent research, we explore how DBNs have been applied in IDS contexts, examine optimization methods like layer-wise pre-training and dropout regularization that contribute to better detection performance, and evaluate commonly used benchmark datasets including UNSW-NB15, NSL-KDD, and CSE-CIC-IDS2018. This study compiles empirical evidence to assess DBNs' performance across varied network conditions and traffic types. Findings suggest that DBNs are effective in learning complex data patterns and improving the detection of anomalies. Nonetheless, challenges such as interpretability, high computational requirements, and the limitations of existing datasets continue to hinder widespread adoption. This work adds to the ongoing cybersecurity discourse by outlining major developments, constraints, and future directions for DBN-powered IDS. It ends by proposing strategic improvements, including the development of more efficient models, broader dataset coverage, and real-time, adaptive integration to support smarter and more responsive IDS solutions.

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