An Adaptive Machine Learning Approach for Network Intrusion Detection

Abstract

The increasing complexity and frequency of cyberattacks have heightened the need for intrusion detection systems capable of recognizing threats that extend beyond predefined signatures. Traditional signature-based IDS solutions are often insufficient for detecting newly emerging or evolving attack patterns. This work presents a supervised machine learning approach for network intrusion detection, employing the NSL-KDD dataset to classify network traffic as either normal or malicious. The study investigates the performance of several classifiers, comprising Decision Trees, Random Forest, Logistic Regression, k-Nearest Neighbors (k-NN), and Support Vector Machines (SVM). The proposed framework incorporates key preprocessing procedures such as feature normalization, dimensionality reduction, and attribute filtering to enhance model robustness. The proposed models are evaluated using accuracy, precision, recall, and F1-score, with special consideration given to minimizing false positives to enhance real-time detection effectiveness. Experimental findings demonstrate that supervised learning models exhibit improved detection capability and better adaptability to unfamiliar attack behaviors. The results highlight the potential of machine learning techniques as scalable and effective components of modern network security architectures.