  Streaming 360{\deg} video demands high bandwidth and low latency, and poses
significant challenges to Internet Service Providers (ISPs) and Mobile Network
Operators (MNOs). The identification of 360{\deg} video traffic can therefore
benefits fixed and mobile carriers to optimize their network and provide better
Quality of Experience (QoE) to the user. However, end-to-end encryption of
network traffic has obstructed identifying those 360{\deg} videos from regular
videos. As a solution this paper presents 360NorVic, a near-realtime and
offline Machine Learning (ML) classification engine to distinguish 360{\deg}
videos from regular videos when streamed from mobile devices. We collect packet
and flow level data for over 800 video traces from YouTube & Facebook
accounting for 200 unique videos under varying streaming conditions. Our
results show that for near-realtime and offline classification at packet level,
average accuracy exceeds 95%, and that for flow level, 360NorVic achieves more
than 92% average accuracy. Finally, we pilot our solution in the commercial
network of a large MNO showing the feasibility and effectiveness of 360NorVic
in production settings.
