Enhancing Intrusion Detection in IoT Botnets using Novel Pay-Offs and Matching Coin Game Comparing with Dominant Game Strategy

Abstract

To enhance the detection of attacks in the IoT botnets using Pay-offs and matching coin game method compared with dominant game strategy method. By using novel Pay-offs and matching coin game method, detection of attacks in the botnets performed using pytorch. The DDoS attacks disrupt the operations of IoT Botnets. The proposed system helps to detect the intrusion in the IoT botnets. It has been tested with several iterations of sample size 10. The performance of the Pay-offs and matching coin game method gives high accuracy than with dominant game strategy method. By using statistical tool SPSS mean accuracy of 92% for the Pay-offs and matching coin game method compared to the dominantly game strategy method, which gives 84% accuracy. On the whole process of prediction of accuracy in detection of intrusion in IoT botnets, the novel Pay-offs and matching coin game method gives significantly better performance compared with dominantly game strategy method.