Machine learning approach for intrusion detection system using dimensionality reduction

Abstract

As cyberspace has emerged, security in all the domains like networks, cloud, and databases has become a greater concern in real-time distributed systems. Existing systems for detecting intrusions (IDS) are having challenges coping with constantly changing threats. The proposed model, DR-DBMS (dimensionality reduction in database management systems), creates a unique strategy that combines supervised machine learning algorithms, dimensionality reduction approaches and advanced rule-based classifiers to improve intrusion detection accuracy in terms of different types of attacks. According to simulation results, the DR-DBMS system detected the intrusion attack in 0.07 seconds and with a smaller number of features using the dimensionality reduction and feature selection techniques efficiently.