International Journal of Scientific Research and Engineering Development

The rapid expansion of internet services and digital communication has significantly increased the risk of cyber attacks on computer networks. Network intrusion detection has become an essential component of modern cybersecurity systems to identify malicious activities and protect sensitive information. Traditional intrusion detection systems rely on signature-based methods, which are ineffective in detecting new and unknown attacks. This paper proposes a machine learning–based Network Intrusion Detection System (NIDS) using the Random Forest algorithm to classify network traffic as normal or malicious. The system is trained and evaluated using the NSL-KDD dataset. Data preprocessing, feature selection, and classification techniques are applied to improve detection accuracy. Random Forest is selected due to its high accuracy, robustness, and ability to handle large network datasets. Experimental results demonstrate that the proposed system achieves reliable intrusion detection and reduces false alarm rates. The study shows that machine learning–based approaches provide efficient and scalable solutions for enhancing network security in modern environments.

Vamil S, Dr.K. Banuroopa,"Network Intrusion Detection Using Machine Learning" International Journal of Scientific Research and Engineering Development, V9(1): Page(2136-2142) Jan-Feb 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.