Machine Learning-Based Intrusion Detection Systems: A Comprehensive Review

Abstract

The proliferation of networked systems, cloud computing infrastructures, Internet of Things (IoT) ecosystems, and distributed enterprise architectures has dramatically expanded the attack surface available to cyber adversaries, making intrusion detection a critical component of modern cybersecurity defense. Traditional intrusion detection systems (IDS) based on signature matching and static rule sets are increasingly inadequate against the sophistication, polymorphism, and sheer volume of contemporary cyber threats. Machine learning (ML) has emerged as a transformative paradigm for intrusion detection, offering data-driven approaches capable of learning complex attack patterns from network traffic and system behavior, detecting previously unseen threats through anomaly identification, and adapting to the continuously evolving threat landscape. This paper presents a comprehensive review of recent advances in machine learning-based intrusion detection systems. We examine the foundational ML methodologies applied to intrusion detection, including supervised classification, unsupervised anomaly detection, deep learning architectures, ensemble methods, and reinforcement learning. The paper provides a systematic analysis of IDS architectures categorized by detection methodology (signature-based, anomaly-based, hybrid), deployment context (network-based, host-based, cloud-based, IoT-oriented), and learning paradigm (batch, online, federated). We review the benchmark datasets that have shaped research progress, including NSL-KDD, UNSW-NB15, CICIDS, and CSE-CIC-IDS, and critically assess their strengths, limitations, and relevance to contemporary network environments. Applications across enterprise networks, industrial control systems, cloud and virtualized environments, IoT networks, and software-defined networking are examined in depth. Critical challenges including class imbalance, high false positive rates, concept drift, adversarial evasion, computational efficiency for real-time deployment, and the scarcity of labeled attack data are systematically analyzed. Finally, we outline future research directions including self-supervised pre-training for network traffic, graph-based lateral movement detection, explainable IDS, federated threat intelligence, and the integration of large language models for automated threat analysis.