D2EBTN: Deep stacked dual learning ensembled attentional-based deep learning model for malware detection in the cloud

Abstract Cloud computing is quite popular because of its scalability and flexibility merits. However, faces significant security risks from malware, which exploits infrastructure weaknesses to cause unauthorized access, identity theft, and data breaches. Most current deep learning approaches struggle to detect malware effectively due to significant challenges like high computational costs, issues with overfitting, lack of interpretability, and the potential for inaccurate results. As a consequence, the research proposes a Deep Stacked Dual Learning Ensembled Attentional Bidirectional Long Short-Term Memory and Deep Belief Network (D2EBTN) for malware detection in the cloud. The D2EBTN utilizes a stacked dual learning strategy with the combination of incremental and federated learning concepts, offering a robust, efficient, and privacy-preserving malware detection across distributed systems. In addition to that, the combination of the ensemble attention module and the deep belief network enhances performance accuracy. Leverages the distinct strengths of both components to address the complex and evolving nature of malware threats, thus addressing existing challenges. Owing to these advancements, the model outperforms state-of-the-art methods and attains a precision of 98.63%, sensitivity of 97.75%, F1-score of 98.19%, specificity of 98.41%, and accuracy of 98.04% under the CIC-IDS2017 dataset.