Optimized Network Threat Detection and Classification via Machine Learning
Abstract
In the current digital era, ensuring network security is more critical than ever, with intrusion detection systems (IDS) playing a vital role in defending sensitive information against malicious threats. Conventional IDS, primarily based on signature-matching techniques, often suffer from high false alarm rates, limited adaptability to new types of attacks, and intensive computational requirements. This study focuses on the design and implementation of an effective network intrusion detection and classification system using machine learning methodologies to overcome these limitations. Utilizing well-known datasets such as NSL-KDD and UNSW-NB15, our research integrates various supervised learning models—including Support Vector Machines (SVM), Random Forests, and Neural Networks—combined with detailed preprocessing and feature engineering procedures. Performance evaluation using metrics such as accuracy, precision, recall, and ROC-AUC reveals a notable enhancement in both detection performance and computational efficiency. The outcomes highlight the ability of machine learning-powered IDS to lower false positives and respond more effectively to novel threats, offering a significant advantage over traditional approaches. This work not only emphasizes the capabilities of modern machine learning algorithms in intrusion detection but also lays the groundwork for future innovations in network security. As cyber threats evolve rapidly, the development of intelligent network intrusion detection and classification systems becomes essential for ensuring data protection and maintaining system reliability. This paper introduces a new approach that applies machine learning techniques to significantly improve the detection accuracy and efficiency of IDS. By integrating both supervised and unsupervised learning strategies, the system is capable of detecting and categorizing both previously known and new attack types in real time. Advanced feature selection techniques are employed to enhance model performance, achieving high detection accuracy while keeping false positives to a minimum. Experimental validation on established benchmark datasets shows considerable gains in both detection capabilities and processing times when compared to traditional IDS solutions. The proposed method not only reinforces network security but also offers a flexible and scalable framework capable of addressing future cybersecurity threats.
Keywords
Intrusion Prevention, Feature Selection, Real-time Detection, Threat Detection, Network Intrusion Detection