EHoSAD: Elk Herd Optimizer for Sybil Attack Detection in Internet of Things

In the Internet of Things (IoT), Sybil attacks pose significant security vulnerabilities that can severely disrupt IoT operations by generating duplicate and fraudulent identities and fake routes. In this paper, we used a Novel Elk Herd Optimizer (EHO) with the help of Trust estimation to detect the Sybil attack in IoT. The model employs combined direct and indirect trust metrics for Sybil detection, strengthening the network's resistance. The EHO algorithm successfully explores and exploits through its optimal trust threshold calculation process, which accurately identifies malicious nodes. Simulation results show that the proposed EHoSAD outperforms the existing baseline approaches.