Enhancing intrusion detection in wireless sensor networks using the sperm swarm optimization algorithm

The fundamental ideas and thoughts at the back of the proposed algorithm are stimulated by using the motion of sperm to fertilize an egg. In a sperm swarm, the sperm swarm actions ahead from a low-temperature vicinity known as the cervix. During this direction, sperm search for a high-temperature vicinity known as the fallopian tubes in which the egg is looking ahead to the swarm to fertilize in this vicinity, for which this vicinity is taken into consideration as the most useful solution. This paper proposes the Sperm Swarm Optimization (SSO) algorithm to enhance the performance of intrusion detection systems (IDS) in wireless sensor networks (WSNs). Inspired by the sperm's motion toward fertilization, the SSO algorithm optimizes sensor networks by reducing end-to-end delays and improving energy efficiency. The proposed model, evaluated using the KDDCup dataset, achieved a classification accuracy of 95.3%, surpassing traditional Support Vector Machine (SVM)-based methods. This study demonstrates that SSO can effectively improve WSN security by enhancing intrusion detection while optimizing network performance.