Enhancing intrusion detection systems through dimensionality reduction: A comparative study of machine learning techniques for cyber security

Abstract

Our research aims to improve automated intrusion detection by developing a highly accurate classifier with minimal false alarms. The motivation behind our work is to tackle the challenges of high dimensionality in intrusion detection and enhance the classification performance of classifiers, ultimately leading to more accurate and efficient detection of intrusions. To achieve this, we conduct experiments using the NSL-KDD data set, a widely used benchmark in this domain. This data set comprises approximately 126,000 samples of normal and abnormal network traffic for training and 23,000 samples for testing. Initially, we employ the entire feature set to train classifiers, and the outcomes are promising. Among the classifiers tested, the J48 tree achieves the highest reported accuracy of 79.1 percent. To enhance classifier performance, we explore two projection approaches: Random Projection and PCA. Random Projection yields notable improvements, with the PART algorithm achieving the best-reported accuracy of 82.0 %, outperforming the original feature set. Moreover, random projection proves to be more time-efficient than PCA across most classifiers. Our findings demonstrate the effectiveness of random projection in improving intrusion detection accuracy while reducing training time. This research contributes valuable insights to the cybersecurity field and fosters potential advancements in intrusion detection systems.