Advancing Intrusion Detection Systems: A Review of Machine Learning and Deep Learning Approaches

Abstract

In contemporary computer networks, the utilisation of intrusion detection systems (IDS) plays a vital role in fortifying the security posture against the continuously expanding array of cyber threats. Nevertheless, the effectiveness of traditional rule-based Intrusion Detection Systems (IDS) in detecting and mitigating contemporary threats is hindered by their inherent complexity and sophistication. The application of machine learning (ML) and deep learning (DL) techniques in intrusion detection systems (IDS) has garnered considerable interest as a means to tackle this challenge. The objective of this review paper is to investigate the application of machine learning (ML) and deep learning (DL) methodologies in intrusion detection systems (IDS) with the purpose of improving accuracy, efficiency, and robustness.The paper begins by presenting a comprehensive introduction to conventional Intrusion Detection Systems (IDS) and underscoring their inherent limitations, thereby emphasising the imperative need for more sophisticated Machine Learning (ML) and Deep Learning (DL) methodologies. The text underscores the significance of machine learning and deep learning-based intrusion detection systems (IDS) in addressing the ever-changing landscape of cyber threats. Additionally, it explores prevalent architectures utilised in this domain, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Furthermore, the review examines the utilisation of transfer learning and pretraining methods, which have the potential to mitigate the problem of limited data availability and enhance the ability of intrusion detection system models to generalise.

This review paper offers a thorough examination of machine learning (ML) and deep learning (DL) methodologies within the context of intrusion detection systems (IDS). The text underscores the necessity for employing sophisticated methodologies to address the constraints associated with conventional Intrusion Detection Systems (IDS). It delves into multiple facets encompassing architectures, datasets, preprocessing techniques, machine learning (ML) algorithms, and evaluation metrics. The paper additionally provides insights into emerging trends and potential areas for future research in machine learning and deep learning-based intrusion detection systems. It emphasises the significance of addressing crucial challenges in order to facilitate progress in the field of intrusion detection..