Abstract
Most of the Internet traffic is encrypted, and the challenge is its ability to recognize the streaming videos from the Internet traffic. In this paper, we present a methodology named traffic pattern plot (TPP) to identify video streams in encrypted network traffic. The proposed methodology plots the video traffic flows and uses a convolutional neural network (CNN) to detect the videos. The results show that the traffic pattern plot generated from 120 s of sniffing network traffic is enough to identify the video even in the encrypted network traffic with 94% accuracy.
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Acknowledgements
This material is based on work supported by the National Cybersecurity Center (NCCS), Pakistan, and Higher Education Commission (HEC) under grant RF-NCCS-023.
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Kamal, A.S., Bukhari, S.M.A.H., Khan, M.U.S., Maqsood, T., Fayyaz, M.A.B. (2023). Traffic Pattern Plot: Video Identification in Encrypted Network Traffic. In: Nagar, A.K., Singh Jat, D., Mishra, D.K., Joshi, A. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 579. Springer, Singapore. https://doi.org/10.1007/978-981-19-7663-6_8
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DOI: https://doi.org/10.1007/978-981-19-7663-6_8
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