An Improved Harris Hawks Optimization Algorithm for Malware Intrusion Detection in Cloud Storage
Keywords:
Cloud Data Security, Malware Intrusion Detection, Multilayer Perceptron Model, Harris Hawks Optimization, Unknown Malware AttacksAbstract
Defending against malware threats has become a crucial concern in the continually evolving realm of cloud computing. Creative malware has become a rising challenge to cloud data security. Hence, we have proposed a methodology for Cloud Malware Intrusion Detection to strengthen cloud infrastructures against the constant threat of unknown malware attacks. Harris Hawks Optimization (HHO) and the Multilayer Perceptron (MLP) are proposed as a hybrid approach to tackling malware attacks in the cloud using deep learning to detect subtle abnormalities as an indicative solution to identify hidden malware threats. Hence, the MLP model provides part of a vigilant data monitoring system inside the cloud architecture. By modifying the MLP's hyperparameters, HHO serves as an optimization engine, enhancing the MLP's accuracy and efficiency. This hybrid approach has the capacity to safeguard cloud resources against the constantly changing and threatening landscape of malware, which provides an efficient and adaptive defense system, as proved by the implementation process. Accuracy of 98.45%, precision of 98.55%, recall of 99.88%, and F1 Score of 99.21% have been achieved during the experimentation.
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