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 cloudbased 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 (DADense-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.