Machine learning-Based Intrusion Detection System for Network Attacks

The rapid growth of interconnected networks, cloud platforms, IoT devices, and distributed systems has increased both efficiency and vulnerability to cyberattacks. Traditional signaturebased Intrusion Detection Systems (IDS) struggle to detect unknown or evolving threats due to static rules. Machine Learning (ML)–based IDS offer an intelligent, data-driven approach capable of identifying complex attack patterns and adapting to changing network conditions. This study presents an ML-based IDS framework incorporating data preprocessing, feature selection, and multiple classifiers. Experiments on benchmark datasets demonstrate improved accuracy, robustness, and scalability compared to conventional IDS, highlighting ML’s potential as a core component of modern network security.