Machine Learning Based Security Solutions in MANETs: State of the art approaches
Renu Popli, Monika Sethi, Isha Kansal, Atul Garg, Nitin Goyal
Abstract
Abstract Machine learning (ML) techniques provide the learning capability to a system and encourage adaptation into the environment, based upon many logical and statistical operations. The prime goal of ML is to recognize the complex patterns and make decisions based on the results. There are various ML algorithms which are implemented to secure the mobile ad-hoc networks. The infrastructure-less environment of MANETs poses a great challenge in implementation of the security systems. The security approaches in MANETs mainly focus on intrusion detection, malicious attacks mitigation, elimination of outlier/misbehavior/selfish nodes and securing routing paths. The researchers have been using cutting edge technologies for providing efficient security solutions by taking into the consideration of dynamic environment of MANETs. These technologies include machine learning, Artificial Intelligence (AI), Genetic Algorithms based methods, biological-inspired algorithms and so on. This paper presents a comprehensive and systematic study of various modern approaches for intensifying security in MANETs.