Machine Learning-Based Intrusion Detection Systems for Enhanced Network Security

Abstract

The growing complexity of network infrastructures and the increasing sophistication of cyberattacks have exposed the limitations of traditional intrusion detection mechanisms. Conventional signature-based Intrusion Detection Systems (IDS) are effective against known attack patterns but often fail to detect zero-day exploits and rapidly evolving threats. Machine learning (ML) has emerged as a promising approach for enhancing intrusion detection by enabling adaptive, data-driven, and intelligent threat identification. This paper presents a comprehensive review of machine learning-based intrusion detection systems, covering supervised, unsupervised, semi-supervised, and deep learning techniques used to detect network intrusions. The review examines widely adopted benchmark datasets, feature selection and engineering methods, and evaluation metrics for assessing IDS performance. It also discusses different deployment architectures, including centralized, distributed, edge-based, and hybrid IDS frameworks, highlighting their advantages and limitations in modern network environments. Additionally, two conceptual models—a machine learning-based intrusion detection pipeline and a layered network security architecture—are presented to demonstrate the integration of ML techniques into intelligent IDS solutions. The paper further analyzes current research challenges, including class imbalance, scalability, adversarial machine learning attacks, high computational overhead, data privacy, and real-time detection requirements. Finally, emerging research directions, including explainable artificial intelligence (XAI), federated learning, continual learning, zero-trust security, and edge intelligence, are discussed as key enablers of next-generation intrusion detection systems. This review provides researchers and cybersecurity practitioners with a comprehensive overview of recent advances, existing challenges, and future opportunities in developing robust, scalable, and intelligent machine learning-based intrusion detection systems for securing modern network infrastructures.