Fingerprinting Technique for YouTube Videos Identification in Network Traffic
Recently, many video streaming services, such as YouTube, Twitch, and Facebook, have contributed to video streaming traffic, leading to the possibility of streaming unwanted and inappropriate content to minors or individuals at workplaces. Therefore, monitoring such content is necessary. Although th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9832901/ |
| _version_ | 1818002181110890496 |
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| author | Waleed Afandi Syed Muhammad Ammar Hassan Bukhari Muhammad U. S. Khan Tahir Maqsood Samee U. Khan |
| author_facet | Waleed Afandi Syed Muhammad Ammar Hassan Bukhari Muhammad U. S. Khan Tahir Maqsood Samee U. Khan |
| author_sort | Waleed Afandi |
| collection | DOAJ |
| description | Recently, many video streaming services, such as YouTube, Twitch, and Facebook, have contributed to video streaming traffic, leading to the possibility of streaming unwanted and inappropriate content to minors or individuals at workplaces. Therefore, monitoring such content is necessary. Although the video traffic is encrypted, several studies have proposed techniques using traffic data to decipher users’ activity on the web. Dynamic Adaptive Streaming over HTTP (DASH) uses Variable Bit-Rate (VBR) - the most widely adopted video streaming technology, to ensure smooth streaming. VBR causes inconsistencies in video identification in most research. This research proposes a fingerprinting method to accommodate for VBR inconsistencies. First, bytes per second (BPS) are extracted from the YouTube video stream. Bytes per Period (BPP) are generated from the BPS, and then fingerprints are generated from these BPPs. Furthermore, a Convolutional Neural Network (CNN) is optimized through experiments. The resulting CNN is used to detect YouTube streams over VPN, Non-VPN, and a combination of both VPN and Non-VPN network traffic. |
| first_indexed | 2024-04-14T03:43:24Z |
| format | Article |
| id | doaj.art-777031217670418184ee190c42a20410 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-14T03:43:24Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-777031217670418184ee190c42a204102022-12-22T02:14:25ZengIEEEIEEE Access2169-35362022-01-0110767317674110.1109/ACCESS.2022.31924589832901Fingerprinting Technique for YouTube Videos Identification in Network TrafficWaleed Afandi0Syed Muhammad Ammar Hassan Bukhari1https://orcid.org/0000-0002-6359-5879Muhammad U. S. Khan2https://orcid.org/0000-0002-7299-621XTahir Maqsood3Samee U. Khan4https://orcid.org/0000-0001-8650-4354Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, PakistanDepartment of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USARecently, many video streaming services, such as YouTube, Twitch, and Facebook, have contributed to video streaming traffic, leading to the possibility of streaming unwanted and inappropriate content to minors or individuals at workplaces. Therefore, monitoring such content is necessary. Although the video traffic is encrypted, several studies have proposed techniques using traffic data to decipher users’ activity on the web. Dynamic Adaptive Streaming over HTTP (DASH) uses Variable Bit-Rate (VBR) - the most widely adopted video streaming technology, to ensure smooth streaming. VBR causes inconsistencies in video identification in most research. This research proposes a fingerprinting method to accommodate for VBR inconsistencies. First, bytes per second (BPS) are extracted from the YouTube video stream. Bytes per Period (BPP) are generated from the BPS, and then fingerprints are generated from these BPPs. Furthermore, a Convolutional Neural Network (CNN) is optimized through experiments. The resulting CNN is used to detect YouTube streams over VPN, Non-VPN, and a combination of both VPN and Non-VPN network traffic.https://ieeexplore.ieee.org/document/9832901/Video identificationfingerprintingdeep learningclassificationvariable bitrate |
| spellingShingle | Waleed Afandi Syed Muhammad Ammar Hassan Bukhari Muhammad U. S. Khan Tahir Maqsood Samee U. Khan Fingerprinting Technique for YouTube Videos Identification in Network Traffic IEEE Access Video identification fingerprinting deep learning classification variable bitrate |
| title | Fingerprinting Technique for YouTube Videos Identification in Network Traffic |
| title_full | Fingerprinting Technique for YouTube Videos Identification in Network Traffic |
| title_fullStr | Fingerprinting Technique for YouTube Videos Identification in Network Traffic |
| title_full_unstemmed | Fingerprinting Technique for YouTube Videos Identification in Network Traffic |
| title_short | Fingerprinting Technique for YouTube Videos Identification in Network Traffic |
| title_sort | fingerprinting technique for youtube videos identification in network traffic |
| topic | Video identification fingerprinting deep learning classification variable bitrate |
| url | https://ieeexplore.ieee.org/document/9832901/ |
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