Sailfish-cat algorithm-enhanced generative adversarial network for attack detection in internet of things-Fog network authentication

Abstract

The internet of things (IoT) has emerged as a prominent and influential concept within the realm of computing. Various attack detection methods are devised for detecting attacks in IoT-Fog environment. Despite all these efforts, attack detection still remained as a challenging task due to factors such as low latency, resource constraints of IoT devices, scalability issues, and distribution complexities. All these challenges are addressed in this paper by designing an efficient attack detection technique named as sailfish- cat optimization-based generative adversarial network (SaCO-based GAN) tailored for the IoT-Fog framework. This proposed approach introduces the SaCO-based GAN for IoT-Fog attack detection utilizing deep learning and feature-based classification, validated through experiments showing superior performance metrics. Notably, the SaCO optimization technique is utilized to train the GAN. Experimental results demonstrate the efficacy of the SaCO-based GAN with a maximum recall of 92.15%, a maximum precision of 91.21%, and a maximum F-Measure of 92.16%, outperforming existing techniques in IoT-Fog attack detection. The paper recommends enhancing scalability, implementing real-time detection strategies, rigorously testing robustness against diverse attack scenarios, and integrating with existing IoT security frameworks for practical deployment.