Abstract: This project presents the development of an Intrusion Detection System (IDS) using machine learning techniques to identify and classify potential threats in network traffic. Leveraging the NSL-KDD dataset, which provides a refined and widely accepted benchmark for network intrusion detection research, the system is trained to detect various types of attacks such as DoS, probe, R2L, and U2R. The project involves preprocessing the dataset, feature selection, and applying supervised learning algorithms like Decision Trees, Random Forest, and Support Vector Machines to build an accurate classification model. The goal is to enhance network security by enabling early detection of malicious activities and reducing false positive rates, ultimately providing a reliable and scalable solution for real-time threat detection in modern network environments.
Keywords: Intrusion Detection System (IDS), Machine Learning, NSL-KDD Dataset, Network Security, Supervised Learning, Random Forest, Feature Selection, Anomaly Detection, Cybersecurity, Attack Classification.
|
DOI:
10.17148/IJARCCE.2025.14627
