MANET: A SURVEY ON MACHINE LEARNING-BASED INTRUSION DETECTION APPROACHES

A Mobile Ad-hoc Network (MANET) is infrastructure less network which is a collection of moving nodes connected dynamically in an arbitrary manner. Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. MANETs are more susceptible to the security attacks because of the node mobility which imposes a set of challenges security issues. To tackle these security issues, such as the use of encryption and authentication techniques, have been proposed as a first line of defense to reduce the risk of security problems. However, such risks cannot be completely eliminated, there is a strong need of intrusion detection systems (IDS) as a second line of defense for securing MANET. An intrusion-detection system (IDS) can be defined as the tools, methods, and resources to help identify, assess, and report unauthorized or unapproved network activity. Machine learning based intrusion detection approaches must be deployed and elaborated to facilitate the identification of attacks and enables system to make decisions on intrusion while continuing to learn about their mobile environment. In this paper, we present the most well-known models for building intrusion detection systems by incorporating machine learning in the MANET scenario.