A Machine Learning-Based Intrusion Detection Framework for Enhanced Network Security

Abstract

The rapid expansion of interconnected networks, cloud computing, Internet of Things (IoT) devices, and digital communication technologies has significantly increased the complexity of modern cyber threats, making traditional network security mechanisms increasingly inadequate. Intrusion Detection Systems (IDS) are essential components of cybersecurity infrastructures, designed to monitor network activities and identify malicious behavior before it compromises system integrity. However, conventional signature-based and rule-based IDS are primarily effective against previously known attack patterns and often fail to detect zero-day attacks, advanced persistent threats (APTs), and other evolving cyber threats. To address these limitations, machine learning (ML) has emerged as a transformative technology that enables adaptive, intelligent, and data-driven intrusion detection by learning complex patterns from network traffic and system behavior. This review presents a comprehensive analysis of machine learning-based intrusion detection systems, covering a wide range of techniques including supervised learning, unsupervised learning, ensemble learning, and deep learning models. The paper examines widely used benchmark datasets, feature selection and feature engineering methods, data preprocessing techniques, and commonly adopted performance evaluation metrics for assessing intrusion detection effectiveness. It also reviews various IDS deployment architectures, including centralized, distributed, edge-based, cloud-enabled, and hybrid frameworks, highlighting their strengths and limitations in different networking environments. To provide a clear understanding of intelligent intrusion detection mechanisms, the review introduces two conceptual frameworks: a machine learning-based intrusion detection pipeline that illustrates the end-to-end process from data acquisition to threat classification, and a layered network security architecture demonstrating the integration of ML techniques into modern cybersecurity infrastructures. Furthermore, the paper discusses critical challenges affecting the deployment of ML-based IDS, including data imbalance, scalability, computational complexity, model interpretability, adversarial machine learning attacks, privacy preservation, and real-time processing constraints.