Enhancing Cloud Computing Security through Deep Learning and Attention Mechanism Intrusion Detection Systems

This research investigates the enhancement of cloud computing security through adaptive neural network intrusion detection systems, employing Deep Belief Networks (DBN) and Attention Mechanism. The study aims to address the growing concerns surrounding security threats in cloud environments by leveraging advanced machine learning techniques. Experimental evaluations were conducted to assess the performance of the proposed approach, with specific emphasis on detection accuracy, false positive rate, and computational efficiency. Results indicate that the DBN-based intrusion detection system achieved an average detection accuracy of $94.4 \%$, with a false positive rate of $2.2 \%$ and a computational efficiency of $\mathbf{1 5 8. 4}$ milliseconds. In comparison, the IDS utilizing Attention Mechanism demonstrated superior performance, with an average detection accuracy of $96.2 \%$, a false positive rate of $1.1 \%$, and a computational efficiency of 153.2 milliseconds. These findings underscore the effectiveness of DBN and Attention Mechanism in bolstering the security posture of cloud computing environments. By integrating adaptive neural network-based intrusion detection systems, organizations can enhance threat detection capabilities, mitigate risks, and safeguard sensitive data and critical assets in the cloud.