GTA-IDS: Game Theoretic Approach to Enhance IDS Detection in Cloud Environment

Abstract

The Internet of Things (IoT) industry is growing with the high-quality collaboration with Cloud Computing. The data generated by the IoT devices is quite large which can be efficiently stored and processed by the cloud. Further, the scenario like COVID-19 led to an unexpected flood of IoT devices on enabling networks to facilitate online services, which increases the potential threats to the companies fighting to remain operational during the crises. Still, the problem with the IoT devices is their weak security implications because vendors prioritize other factors like energy-saving and efficiency at the cost of security. The Attacker can send malicious requests through the vulnerable IoT device to the network and exploit the cloud in various ways. So, to address this issue, a Game Theoretic Approach to enhance IDS detection (GTA-IDS) in Cloud Environment has been devised that helps the Defender system to be more efficient, accurate in decision-making and save energy. The algorithm based on relative information entropy has been developed to defend against such attacks. The Bayesian Nash Equilibrium (BNE) has been used to make the Defender's strategies and perform actions to maximize its payoffs. The model has been tested on the NSL-KDD dataset and the results have been compared to the existing techniques. The results show that despite efforts made by the Attacker, the Defender always gets a better gain and ultimately eliminates the attack.