International Research Journal of Education and Technology

Machine learning (ML) techniques allow systems to learn based on many logical and statistical operations, helping them adapt to the environment. The main goal of ML is to recognize complex patterns and make decisions based on the results. There are various ML algorithms that have been implemented to secure mobile ad-hoc networks. The infrastructure less environment of MANETs poses great challenges in implementing security systems. Security approaches in MANETs are primarily focused on intrusion detection, countering malicious attacks, eliminating outliers/misbehaving/selfish nodes, and protecting routing paths. Researchers use state-of-the-art technologies to provide efficient security solutions considering the dynamic environment of MANETs. These technologies include machine learning, artificial intelligence (AI), genetic algorithm-based techniques, biologically inspired algorithms, etc. In this article, we present a comprehensive and systematic study of various state-of-the-art approaches to enhance the security of MANETs.

Key Words: Machine Learning (ML), Mobile Ad-Hoc Networks (MANETs), Intrusion Detection Systems (IDS), Malicious Attacks, Routing Security