Machine learning approach for intrusion detection system using dimensionality reduction

Deepa Manikandan (SRM Institute of Science and Technology)
Jayaseelan Dhilipan (SRM Institute of Science and Technology)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 34, No 1: April 2024

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.