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

Shaweta Sachdeva () and Aleem Ali ()
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Shaweta Sachdeva: Glocal University Saharanpur
Aleem Ali: Glocal University Saharanpur

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 1, No 16, 156-165

Abstract: 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.

Keywords: DDoS; Attack Classification; Machine learning; Digital forensic process; Cloud network; Network security; Attack classification (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-021-01323-4

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