Advances in nano research

  • 제15권6호
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  • Pages.513-520
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  • 2023
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  • 2287-237X(pISSN)
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  • 2287-2388(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

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)
  • 투고 : 2023.02.14
  • 심사 : 2023.09.27
  • 발행 : 2023.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

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

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