BSA-SGRU-A Novel Deep Learning Framework for Alleviate the Multiple Attacks in IoT-Cloud Environment

Abstract

The Internet of Things (IoT) has emanated as an innovative technology that grants users to establish connections to external servers located in the cloud and the uninterrupted Internet, allowing them to live more comfortably. As the number of users increases, security and privacy breaches also increase exponentially, as it invests the greatest threats to the IoT networked devices and cloud storage. Though machine and deep learning algorithms have paved the bright light toward the design of intelligent systems to mitigate the attacks, creating effective defenses against numerous assaults remains a significant design challenge for researchers, and accurately predicting these attacks continues to pose an ongoing hurdle. To solve this problem, this paper proposes a hybrid learning model that ensembles the skip connection-based gated recurrent units (Skip-GRU) and bi-layered self-attention networks to predict and mitigate the different attacks. First, Skip-GRU is constructed using residual connections that remove redundant information and capture only global features. Second, novel bi-layered privacy-preserving networks are combined to obtain the spatial and temporal attributes. Finally, softmax is utilized to obtain the prediction results of sample labels. Performance parameters including accuracy, precision, recall, and F1-score are assessed and evaluated during the comprehensive eradication exploration that is conducted utilizing the NSL-KDD and UNSW datasets. By comparing how it performs to other cutting-edge approaches to learning, the one recommended demonstrates its superiority. The technique has proven its efficacy in providing enough security versus various threats, as seen by the results, which show that it has obtained 0.97 accuracy, 0.96 precision, 0.96 recall, and 0.96 F1-score.