Malicious behavior identification using Dual Attention Based dense bi-directional gated recurrent network in the cloud computing environment

The rapid expansion of novel computing technologies has driven organizations to collaborate through cloud-based platforms, making robust security frameworks to ensure integrity, security, and accessibility. This paper proposes a deep learning approach to enhance malicious behaviour detection in cloud environments. Initially, the input data undergoes pre-processing using Min-Max Normalization, Missing Value Imputation, and Data Reduction to eliminate noise and inconsistencies. Feature selection is performed using the Improved Cheetah Optimization (ICO) algorithm. Finally, a Dual Attention-Based Dense Bi-Directional Gated Recurrent Unit (DA-Dense-BiGRU) is then employed to detect and classify malicious activity. The proposed approach is evaluated on five distinct datasets, achieving good accuracy rates of 99.35 %, 99.5 %, 99.4 %, 99.2 %, and 98.8 %. These results indicate the model's ability to detect harmful activities and improve security monitoring in cloud computing environments.