Machine learning-based hybrid SSO-MA with optimized secure link state routing protocol in Manet

A decentralized network made up of mobile nodes is termed the Mobile Ad-hoc Network (MANET). Mobility and a finite battery lifespan are the two main problems with MANETs. Advanced methods are essential for enhancing MANET security, network longevity, and energy efficiency. Hence, selecting an appropriate cluster. The cluster's head further boosts the network's energy effectiveness. As a result, a Hybrid Swallow Swarm Optimisation-Memetic Algorithm (SSO-MA) is suggested to develop the energy efficiency & of the MANET network. Then, to secure the network Abnormality Detection System (ADS) is proposed. The MATLAB-2021a platform is used to implement the suggested technique and conduct the analysis. In terms of network performance, the suggested model outperforms the current Genetic Algorithm, Optimised Link State Routing protocol, and Particle Swarm Optimisation techniques. The performance of the model has a minimum delay in the range of 0.82 seconds and a Packet Delivery Ratio (PDR) of 99.82%. Hence, the validation shows that the Hybrid SSO-MA strategy is superior to the other approaches in terms of efficiency.