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Machine learning with digital forensics for attack classification in cloud network environment

Author

Listed:
  • Shaweta Sachdeva

    (Glocal University Saharanpur)

  • Aleem Ali

    (Glocal University Saharanpur)

Abstract

In this paper, various Distributed Denial of service attacks like Internet Control Message Protocol Attack, Transmission Control Protocol Sync Attack, and User Datagram Protocol Attack were considered for data classification. With digital forensics, attack detection faced a new challenge by the exponential growth of network traffic and its many forms on the Internet. The highest True Negative Rate, accuracy, and precision are calculated in this paper. We propose an Attack Classification in Cloud Network Environment method based on machine learning with a digital forensic process. True Negative Rate, accuracy, and precision are all excellent in our detection process, according to our findings. Therefore, our proposed fusion (Digital Forensics based on deep learning) algorithm works well as a data classification detective. Our model performed state-of-the-art attack detection techniques in terms of overall detection performance, detection stability, and system generalization capability.

Suggested Citation

  • Shaweta Sachdeva & Aleem Ali, 2022. "Machine learning with digital forensics for attack classification in cloud network environment," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 156-165, March.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01323-4
    DOI: 10.1007/s13198-021-01323-4
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