Firefly cyclic golden jackal optimisation algorithm with wavelet artificial neural network for blackmailing attack detection in mobile ad-hoc network

Wireless networks called mobile ad hoc networks (MANETs) have an enlarged number of peer nodes. In recent studies, the major challenges are poor false positive rate, minimum detection rate and energy efficiency with higher delay to enhance the security of MANET. To overcome the problem, in this work, the fuzzy clustering model forms the clusters in MANET. The most appropriate cluster heads are selected in the presence of the firefly cyclic golden jackal optimisation (FCGJO) algorithm thereby solving the issues of energy and mobility of nodes. Wavelet Artificial Neural Network model to detect a blackmailing attack in MANET. The NS-2 simulation tool handles the implementation works and the statistical parameters such as attack detection rate, delay, energy consumption, throughput, memory consumption and etc. to compute the performance of proposed approaches. While comparing to the state-of-art studies, the statistical parametric results reveal the proposed intrusion detection performance.