AI-Based Intrusion Detection Systems Using Machine Learning: A Comprehensive Review

The rapid growth of networked systems and digital communication has significantly increased the risk of cyber threats, making Intrusion Detection Systems (IDS) a critical component of modern cybersecurity. Traditional IDS techniques, primarily based on signature matching, are often ineffective against evolving and unknown attacks. To address these limitations, the integration of Machine Learning has emerged as a powerful solution for intelligent and adaptive intrusion detection. This paper presents a comprehensive review of AI-based IDS using machine learning techniques, covering supervised, unsupervised, deep learning, and ensemble approaches. It analyzes widely used benchmark datasets such as KDD Cup 99, NSL-KDD, CICIDS2017, and UNSW-NB15, along with key performance evaluation metrics including accuracy, precision, recall, and false positive rate. The study also highlights major challenges such as data imbalance, redundancy, and lack of real-world applicability. Furthermore, recent advancements and emerging trends, including hybrid models and real-time intrusion detection, are discussed. The review aims to provide a structured understanding of current methodologies and identify research gaps, thereby guiding future research toward the development of more efficient, scalable, and robust intrusion detection systems