Sinkhole Attack Detection in WSN Using Swarm Intelligence Optimization Algorithm

Over the last few years, modern technology has emerged rapidly in almost every aspect of our lives. In the field of wireless communication and the Internet of Things (IoT), Wireless Sensor Networks (WSN) have gained a growing interest from researchers and organizations from all over the globe due to their importance in wireless information transmission. Despite their promising performance and quality of operation, WSNs are vulnerable to a wide range of security attacks. Among these is a sinkhole attack, which presents a severe threat to the security of WSNs. This thesis proposes and develops a detection mechanism against sinkhole attack by adopting Swarm Intelligence (SI) optimization algorithm. The proposed mechanism combines a weight estimation technique and Artificial Bee Colony (ABC) optimization algorithm in order to enhance detection accuracy of sinkhole attack. The proposed work has been implemented in MATLAB and extensive simulations have been carried out to evaluate its performance in terms of detection accuracy, detection time, convergence speed, packet overhead, and energy consumption. The results show that our proposed mechanism is efficient and robust in detecting sinkhole attack with high detection accuracy rate.