Enhancing Cybersecurity in Wireless Sensor Networks: Sybil Attack Detection With DSSF-OGAN and Swarm Search-Based Deep Learning

This study introduces a novel approach to enhance cybersecurity in Wireless Sensor Networks (WSNs) by detecting Sybil attacks, a significant threat to data integrity in unattended environments. Leveraging a Discrete Salp Swarm Fused Optimal Generative Adversarial Network (DSSFOGAN), our method achieves exceptional performance metrics, including an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{F 1}$</tex>-score of 0.97, Recall of 0.96, accuracy of 0.99, precision of 0.98, and an attack detection rate of 0.95. Through rigorous quantitative analysis, particularly focusing on the efficacy of DSSF-OGAN, our research significantly advances cybersecurity measures in WSNs, underscoring the critical need for effective Sybil attack detection mechanisms. By providing a robust foundation for Sybil attack detection and node performance analysis, our findings contribute to the development of resilient cybersecurity strategies in WSN deployments, ensuring the integrity and dependability of data transmission in critical applications.