Lu, Jiping, authorPartridge, Craig, advisorGersch, Joseph, committee memberHayne, Stephen, committee member2021-09-062021-09-062021https://hdl.handle.net/10217/233719With Internet traffic exponentially growing, it is critical for operators to identify voice call and video conference traffic and ensure their quality. Yet, the widespread deployment of encryption protocols makes it challenging to classify encrypted traffic. This research achieves a significant discovery of audio and video traffic transmission rules. Based on the rules, this paper proposes a general audio and video traffic identification algorithm. In order to evaluate this algorithm's performance, we designed and implemented an audio and video traffic identification algorithm in P4 (Programming Protocol-Independent Packet Processors). This promising research reveals that this algorithm achieves 98.98% accuracy on voice call identification. For video conferences, this algorithm achieves 96.24% accuracy on audio data identification and 88.75% accuracy on video data identification. Compared to current pervasive machine learning-based traffic classification approaches, this innovative algorithm bypasses complicated machine learning processes by directly applying audio and video traffic transmission rules on network functions, consuming less computation and memory resources.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.encrypted voice and video traffic identificationaudio and video traffic transmission rulesmultimedia traffic identification and classificationText