NCGTM: A Noncooperative Game-Theoretic Model to Assist IDS in Cloud Environment

Cloud computing has proved to be a high-quality delivery paradigm over the past years. Some of the devices linked with the Cloud have weak security implications. The attacker can inject malware to disrupt the network and compromise the Cloud servers. An intrusion detection system (IDS) used to detect malicious activities in the network needs an improvement over the detection rate (DR) and false positive rate (FPR). In this article, a game-theoretic model has been devised to evaluate the attacker and the defender's strategies and assist the IDS in detecting the attack. The Nash equilibrium has been calculated using graphical methods to conclude the game. The proposed model is validated on real dataset using a machine learning stacked ensemble framework. With the proposed model, the results show an increase in the DR by 0.23, 0.03, and 0.05 percent, an increase in payoff by 0.22, 0.199, and 0.126 percent, a decrease in the FPR by 0.02, 0.03, and 0.07 percent of the IDS as compared to the other models.