Title: An investigation of machine learning-based intrusion detection system in mobile ad hoc network

Authors: C. Edwin Singh; S. Maria Celestin Vigila

Addresses: Department of CSE, Noorul Islam Centre for Higher Education, Nagercoil, Tamil Nadu-629180, India ' Department of Information Technology, Noorul Islam Centre for Higher Education, Tamil Nadu-629180, India

Abstract: Building stable networks is one of the most demanding issues in the current era, as the world is increasingly reliant on computers and technology. The standard MANET protocols, software, and facilities presume a collaborative and networking atmosphere that does not consider protection. Intrusion detection systems (IDS) that track centralised network operations and detect malicious nodes are often used to supplement certain security because mitigation strategies are never sufficient. This study describes ML techniques for distributing valuable properties to IDS for green smart transportation on MANET. The performance of ML-IDSs and a review of their adequacy in MANETs help the users determine intrusion when learning about the MANET context. ML optimised KDD IDS. Ensemble learning in this IDS process gave anomaly scores to controlled packets. Our solution to actual MANET dataset shortages is this ML technique. ML techniques, simulation, and a functioning prototype had created a more resilient IDS for green smart transportation. ML-enhanced IDS detected and reduced MANET harmful activity. This research expanded IDS knowledge through ubiquitous learning.

Keywords: mobile ad hoc network; MANET; IDS; QoS; machine learning; ML; security; attacks.

DOI: 10.1504/IJIEI.2023.130704

International Journal of Intelligent Engineering Informatics, 2023 Vol.11 No.1, pp.54 - 70

Received: 16 Nov 2022
Accepted: 30 Jan 2023
Published online: 03 May 2023 *