Deep embedded median clustering for routing misbehaviour and attacks detection in ad-hoc networks.

Due to the properties of ad-hoc networks, it appears that designing sophisticated defence schemes with more computing capital is impossible in most situations. Recently, an inconsistency in the ad-hoc design of intrusion detection in the network has gotten a lot of coverage, with these intrusion detection techniques operating in either cluster-based or host-based configurations. The host and cluster-based systems have advantages and disadvantages, such as the network preserve security in case of delay in replacing a cluster head. Many detection systems in these networks use a supervised learning method to learn from shared routing knowledge. Deep learning is the trending supervised learning method which is been suggested for many applications, due to its deep feature extraction and classification capability. The deep learning method is best suitable to resolve the problems of the ad hoc network. But due to its limitation of supervised learning nature, more research finds are needed before implementation. These intelligence methods need a massive labeled dataset to self-train and take a decision in real-time. Also, these methods will be vulnerable to new attacks. To address the issues posed, the deep learning approach requires a technique of incorporating unsupervised learning behaviours. This paper proposes and highlights the methodology - Deep Embedded Median Clustering (DEMC), which performs two-phase operations (1) Organization of latent feature space (2) K-median clustering to cluster the Z with Kullback–Leibler divergence as the objective function. Many researchers suggested various methodologies for better anomaly detection in the network, but the knowledge gap and the possibilities for a better solution still exist. This study explores the new possibility and potential of an unsupervised learning technique that works with the nature of deep learning for analyzing and detecting anomalies and intrusion in ad hoc networks. The test to check the DEMC ability has been organized, and the findings are tabulated for analysis. [ABSTRACT FROM AUTHOR]