A Review of Machine Learning-Based Intrusion Detection Systems in Computer Networks

With the rapid expansion of internet technologies and digital communication, computer networks are increasingly exposed to various cyber threats such as malware, phishing, and unauthorized access. Intrusion Detection Systems (IDS) play a crucial role in maintaining network security by monitoring and analyzing network traffic. However, traditional IDS techniques, which are mainly based on signature detection, are limited in their ability to identify new and unknown attacks. To address these challenges, Machine Learning (ML) has emerged as an effective approach for enhancing intrusion detection capabilities. ML-based IDS can automatically learn patterns from network data, detect anomalies, and identify potential threats with higher accuracy. Various techniques such as supervised learning, unsupervised learning, and deep learning are widely used to improve detection performance and reduce false positive rates. This paper presents a comprehensive review of Machine Learning-based Intrusion Detection Systems, highlighting their techniques, advantages, and challenges. It also discusses the limitations of existing approaches and suggests future directions for developing more efficient and intelligent security systems.