Network optimization using defender system in cloud computing security based intrusion detection system withgame theory deep neural network (IDSGT-DNN)

Cloud computing leads an organization for data storage, sharing, processing, and other services. It was subjected to several challenges insecurity due to the presence of regular attacks. These security challenges are worsened due to the presence of various attack environments. The conventional techniques adopted in cloud security are Intrusion Detection System (IDS). However, the IDS system requires an efficient security model for improving security in the cloud. In this paper, to improve the security in the cloud IDS system developed a framework stated as IDSGT-DNN. The proposed model incorporates an attacker and a defender mechanism for attack and normal data processing. The developed game theory is implemented in the DNN model with IWA for the identification of optimal solutions. The estimation is based on the identification of minimal and maximal optimal points in the dataset. The proposed IDSGT-DNN model is evaluated for CICIDS - 2017 dataset. The performance of proposed IDSGT-DNN is evaluated with existing technique is performed. Simulation analysis exhibited that proposed IDSGT-DNN exhibits enhanced performance in terms of accuracy, detection rate, and precision, F-Score, AUC, and FPR.