Attack Detection Scheme based on Blackmailing nodes using Adaptive Tunicate Swarm Algorithm in MANET-IoT Environment

When combined with the IoT, the capabilities of a MANET greatly enhance data acquisition, processing, and sensing. Although many current efforts leverage MANET-IoT and achieve improved outcomes, these approaches have drawbacks like unreliable connectivity due to data integrity problems, scalability challenges, and excessive energy usage. The proposed model focuses on the potential for disruption in the IoT network due to cooperative assaults at the edge nodes and the many ways in which this may occur. An IoT network may be subject to an internal co-operative assault, in which several devices cooperate together to launch a successful cyberattack. Defenses against cooperative assaults cannot be made at the granular level. As a result, with the aid of Edge Computing, a trustworthy, optimization-based environment is created to lessen security risks. The drive of this research is to deliver a Tunicate Swarm Algorithm (TSA) based adaptive metaheuristic algorithm for efficiently solving global optimization problems and locating the malicious nodes in the environment. In each iteration, the proposed Adaptive Tunicate Swarm Algorithm (ATSA) performs two primary tasks: (1) searching the whole search space with a arbitrarily designated tunicate, and (2) refining the search with the location of the finest tunicate. The procedure’s examination capacity is enhanced, and it is protected against premature convergence, thanks to this tweak. Because of how reliable the setting is, any hostile actors or coordinated assaults may be quickly identified and stopped. The typical overhead associated with connecting to the cloud will be reduced, as will any resulting delays. The existence of malicious nodes in an IoT network is avoided and isolated using an ATSA-based strategy. At the end, the effectiveness of the suggested trustworthy environment was verified.