Affiliations: [a] Department of Computer Science, GITAM School of Science, Gandhi Institute of Technology and Management, Visakhapatnam, Andhra Pradesh, India | [b] Department of Information Technology, Rajarshi Shahu College of Engineering, Pune, India | [c] SoCS, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India | [d] Department of Electronics and Telecommunication Engineering, Government College of Engineering, Chandrapur, Maharashtra, India | [e] Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India
Correspondence:
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Corresponding author: Chandra Sekhar Kolli, Department of Computer Science, GITAM School of Science, Gandhi Institute of Technology and Management, Visakhapatnam 530045, Andhra Pradesh, India. E-mail: usercsk@gmail.com.
Abstract: The tremendous development and rapid evolution in computing advancements has urged a lot of organizations to expand their data as well as computational needs. Such type of services offers security concepts like confidentiality, integrity, and availability. Thus, a highly secured domain is the fundamental need of cloud environments. In addition, security breaches are also growing equally in the cloud because of the sophisticated services of the cloud, which cannot be mitigated efficiently through firewall rules and packet filtering methods. In order to mitigate the malicious attacks and to detect the malicious behavior with high detection accuracy, an effective strategy named Multiverse Fractional Calculus (MFC) based hybrid deep learning approach is proposed. Here, two network classifiers namely Hierarchical Attention Network (HAN) and Random Multimodel Deep Learning (RMDL) are employed to detect the presence of malicious behavior. The network classifier is trained by exploiting proposed MFC, which is an integration of multi-verse optimizer and fractional calculus. The proposed MFC-based hybrid deep learning approach has attained superior results with utmost testing sensitivity, accuracy, and specificity of 0.949, 0.939, and 0.947.
Keywords: Malicious behavior detection, fractional calculus, multi-verse optimizer, Hierarchical Attention Network (HAN), Random Multimodel Deep Learning (RMDL)
