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Secure Deep Learning Framework for Cloud to Protect the Virtual Machine from Malicious Events

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Abstract

In recent decades, user communication has been digitalized with some advanced applications. However, securing the digital cloud system is complicated because of the vulnerability of large files and malicious events. Therefore, a present research study intended to design a novel Dragonfly-based Genetic Deep Belief Network (DGDBN) technique to protect the VM from malware activities in the cloud environment. Hence, to validate the presented model, the cloud user files data was considered and imported to the system as input. Then further processes such as preprocessing feature extraction, attack detection and classification were performed. Once the malicious event is predicted, it is neglected by the cloud user environment. Furthermore, implemented novel DGDBN model is tested in the MATLAB programming environment. Finally, the performance parameters like accuracy, precision, reconfiguration time, Recall, F-measure, and data overhead were measured and compared with associated approaches. The novel DGDBN scored the highest accuracy at 99.6%, reduced reconfiguration time at 320 ms and optimized data overhead at 24.2%. Hence, it is in optimal status compared to the existing models.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, 201310, India

    Vinit Kumar

  2. School of Operations and IT, Institute of Public Enterprise, Hyderabad, Telangana, 500101, India

    Shaheen

  3. Department of Computer Science and Engineering, Institute of Aeronautical Engineering, Hyderabad, Telangana, 500043, India

    D. Rajani

  4. Department of Mechanical Engineering, Koneru Lakshamiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522302, India

    Nageswara Rao Medikondu

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  1. Vinit Kumar
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Correspondence to Vinit Kumar.

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Kumar, V., Shaheen, Rajani, D. et al. Secure Deep Learning Framework for Cloud to Protect the Virtual Machine from Malicious Events. Wireless Pers Commun 131, 1859–1879 (2023). https://doi.org/10.1007/s11277-023-10524-y

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