Advances in nano research

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Enhancing cloud computing security: A hybrid machine learning approach for detecting malicious nano-structures behavior

  • Xu Guo (College of Electronics and Information, Shanghai Dianji University) ;
  • T.T. Murmy (Faculty of Computer Engineering, University of Malaya)
  • Received : 2023.02.14
  • Accepted : 2023.09.27
  • Published : 2023.12.25
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Abstract

The exponential proliferation of cutting-edge computing technologies has spurred organizations to outsource their data and computational needs. In the realm of cloud-based computing environments, ensuring robust security, encompassing principles such as confidentiality, availability, and integrity, stands as an overarching imperative. Elevating security measures beyond conventional strategies hinges on a profound comprehension of malware's multifaceted behavioral landscape. This paper presents an innovative paradigm aimed at empowering cloud service providers to adeptly model user behaviors. Our approach harnesses the power of a Particle Swarm Optimization-based Probabilistic Neural Network (PSO-PNN) for detection and recognition processes. Within the initial recognition module, user behaviors are translated into a comprehensible format, and the identification of malicious nano-structures behaviors is orchestrated through a multi-layer neural network. Leveraging the UNSW-NB15 dataset, we meticulously validate our approach, effectively characterizing diverse manifestations of malicious nano-structures behaviors exhibited by users. The experimental results unequivocally underscore the promise of our method in fortifying security monitoring and the discernment of malicious nano-structures behaviors.

Keywords

Acknowledgement

This work was supported by Shahid Rajaee Teacher Training University under grant number 4951.

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