Manchester Metropolitan University's Research Repository

    E-Ensemble: A Novel Ensemble Classifier for Encrypted Video Identification

    Bukhari, Syed MAH, Afandi, Waleed, Khan, Muhammad US, Maqsood, Tahir, Qureshi, Muhammad B, Fayyaz, Muhammad ORCID logoORCID: https://orcid.org/0000-0002-1794-3000 and Nawaz, Raheel (2022) E-Ensemble: A Novel Ensemble Classifier for Encrypted Video Identification. Electronics, 11 (24). p. 4076.

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    In recent years, video identification within encrypted network traffic has gained popularity for many reasons. For example, a government may want to track what content is being watched by its citizens, or businesses may want to block certain content for productivity. Many such reasons advocate for the need to track users on the internet. However, with the introduction of the secure socket layer (SSL) and transport layer security (TLS), it has become difficult to analyze traffic. In addition, dynamic adaptive streaming over HTTP (DASH), which creates abnormalities due to the variable-bitrate (VBR) encoding, makes it difficult for researchers to identify videos in internet traffic. The default quality settings in browsers automatically adjust the quality of streaming videos depending on the network load. These auto-quality settings also increase the challenge in video detection. This paper presents a novel ensemble classifier, E-Ensemble, which overcomes the abnormalities in video identification in encrypted network traffic. To achieve this, three different classifiers are combined by using two different combinations of classifiers: the hard-level and soft-level combinations. To verify the performance of the proposed classifier, the classifiers were trained on a video dataset collected over one month and tested on a separate video dataset captured over 20 days at a different date and time. The soft-level combination of classifiers showed more stable results in handling abnormalities in the dataset than those of the hard-level combination. Furthermore, the soft-level classifier combination technique outperformed the hard-level combination with a high accuracy of 81.81%, even in the auto-quality mode.

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