Anomaly-Based Detection of Black Hole Attacks in Wsn and Manet Utilizing Quantum-Metaheuristic Algorithms

Wireless sensor network (wsn) comprises various distributed nodes that are physically separated. nodes are constantly applying for sensing their environment. if the information sensitivity coefficient is very high, data should be conveyed continually and also with confidentially. wsns have many vulnerability features because of data transferring on the open air, self-organization without reformed structure, bounded range of sources and memory, and limited computing capabilities. therefore, the implementation of security protocols in wsn is inescapable. according to the resemblance between wsn and biotic reaction to the real menace in nature, bio-inspired approaches have variant rules in computer network investigations. in this paper, we exploited an ant colony optimization (aco) algorithm based on ad-hoc on-demand distance vector (aodv) protocol for detection of black hole attacks. finally, the grover quantum metaheuristic algorithm is applied to optimize attack paths’ detection. the results gained from extensive simulations in wsn proved that the proposed approach is capable of improving some fundamental network parameters such as throughput, end-to-end delay, and packet delivery ratio in comparison with other approaches.