Machine learning for intrusion detection in MANET: a state-of-the-art survey

Machine learning consists of algorithms that are first trained with reference input to "learn" its specifics and then used on unseen input for classification purposes. Mobile ad-hoc wireless networks (MANETs) have drawn much attention to research community due to their advantages and growing demand. However, they appear to be more susceptible to various attacks harming their performance than any other kind of network. Intrusion Detection Systems represent the second line of defense against malevolent behavior to MANETs, since they monitor network activities in order to detect any malicious attempt performed by intruders. Due to the inherent distributed architecture of MANET, traditional cryptography schemes cannot completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying machine learning methods for IDS these challenges can be overcome. In this paper, we present the most prominent models for building intrusion detection systems by incorporating machine learning in the MANET scenario. We have structured our survey into four directions of machine learning methods: classification approaches, association rule mining techniques, neural networks and instance based learning approaches. We analyze the most well-known approaches and present notable achievements but also drawbacks or flaws that these methods have. Finally, in concluding our survey we provide some findings of paramount importance identifying open issues in the MANET field of interest.