PPS: A Packets Pattern-based Video Identification in Encrypted Network Traffic

Video identification in encrypted network traffic has become a trending field in the research area for user behavior and Quality of Experience (QoE) analysis. However, the traditional methods of video identification have become ineffective with the usage of Hypertext Transfer Protocol Secure (HTTPS). This paper presents a video identification method in encrypted network traffic using the number of packets received at the user's end in a second. For this purpose, video streams are captured, and feature is extracted from the video streams in the form of a series of Packets per Seconds (PPS). This feature is provided as input to a Convolutional Neural Network (CNN), which learns the pattern from the network traffic feature and successfully identifies the video even if the pattern differs from the training sample. The results show that PPS outperforms the other video identification techniques with a high accuracy of 90%. Moreover, the results show that CNN outperforms its counterpart regarding video identification with a 25% performance increase.