Swarm optimized differential evolution and probabilistic extreme learning based intrusion detection in MANET

MANETs are an attracting mechanism foSr several applications, to name a few being rescue functioning, environmental surveillance and so on due to the reason that they allow users to communicate without the utilization of persistent framework. This pliability, although creates additional security proneness. As a consequence of its advantages and growing insistence, MANETs have fascinated a lot of attentiveness from the scientific research community. In spite of that, exposed or unprotected to be more susceptible to several attacks that perpetrate destruction on their performance than any network. Conventional cryptography mechanisms cannot completely safeguard MANETs in terms of susceptibility owing to dispersed nature however these issues can be addressed by using optimization and deep learning techniques-based Intrusion Detection System (IDS). In this work, we develop a Binary Swarm Optimized Differential and Method of Moments Probabilistic Extreme Learning (BSODMMPEL) node behavior-based IDS for intrusion detection in MANET with minimum training time, misclassification and high precision, accuracy is proposed. Initially, network samples are collected from KDD-CUP-1999 dataset. To ensure robust and significant balance between exploration and exploitation with high probability of convergence to local sub-optima Gudermannian Activation Binary Swarm Optimized Differential Evolutionbased Feature Selection is applied. Next, with the selected features, early detection of intrusion employing Method of Moments Probabilistic Extreme Learning Node Behavior-based IDS is designed. Simulations are performed to validate the result. The performance evaluation in terms of precision, accuracy, sensitivity, training time and misclassification rate show that the proposed method outperforms existing IDS in MANET. The proposed BSOD-MMPEL method is achieved to improve precision of 23 % and accuracy of 27 % and reduce training time of 62 % and misclassification rate of 68 % than existing conventional methods.