Network Intrusion Detection Using Supervised Machine Learning Technique with Feature Selection

Brugumalla Mahendra Achari, Mooramreddy Sreedevi

Abstract: In the increasingly digital world, ensuring the security of computer networks has become a crucial task. This paper introduces a machine learning-based solution to detect potential threats in network activity. By comparing the performance of two supervised learning models-Artificial Neural Networks (ANN) and Support Vector Machines (SVM)-alongside feature selection techniques, we found that ANN, when integrated with wrapper-based feature selection, yielded the highest accuracy on the NSL-KDD dataset. Our findings support the effectiveness of machine learning methods, particularly with refined feature inputs, in building robust and adaptable Network Intrusion Detection Systems (NIDS).

Keywords: Cybersecurity, Machine Learning, Intrusion Detection, Anomaly Detection, ANN, SVM, Network Security, Feature Selection, Real-time Monitoring, False Positives, Attack Patterns

How to Cite?: Brugumalla Mahendra Achari, Mooramreddy Sreedevi, "Network Intrusion Detection Using Supervised Machine Learning Technique with Feature Selection", Volume 14 Issue 7, July 2025, International Journal of Science and Research (IJSR), Pages: 208-212, https://www.ijsr.net/getabstract.php?paperid=SR25701121911, DOI: https://dx.doi.org/10.21275/SR25701121911

Download Citation: APA | MLA | BibTeX | EndNote | RefMan