A Comprehensive Review of Dimensionality Reduction Techniques for Real time Network Intrusion Detection with Applications in Cybersecurity

Abstract

This paper reviews popular signature and anomaly-based intrusion detection systems (IDS). Dimensionality reduction techniques (DRT) are used to increase the efficiency of such systems for real-time operation. Autoencoder-based IDS is rapidly gaining in popularity, primarily due to its inherent ability to denoise data and reduce dimensionality. In addition to the efficiency, we also look at the classification techniques used by various authors, and the overall impact of a model in terms of performance metrics. This article is written for novices in cybersecurityto get a jumpstart on the latest IDS algorithms. The purpose is to give useful insights into the broad and progressive view of various techniques in wide use, expose high-impact future research areas and to summarize classic IDS methods and feature selection techniques.