Network traffic anomaly detection based on packet bytes
Proceedings of the 2003 ACM symposium on Applied computing
Transport layer identification of P2P traffic
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Web tap: detecting covert web traffic
Proceedings of the 11th ACM conference on Computer and communications security
Internet traffic classification demystified: myths, caveats, and the best practices
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
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A widely use of the Internet introduces various online services such as online game, radio online, music online, TV online and video clips, which communicate over Hyper Text Transfer Protocol (HTTP). In this work, we aim to classify an audio and a video traffic from normal web traffic to reduce the misuse of bandwidth consumption. We propose a classification method based on flow information. Our classification use a combination of keyword matching technique and statistical behavior profiles. Keywords are pre-defined by observing from both audio and video traffic. Behavior profiles consist of three attributes, which are the average received packet size, a ratio of number of server-client packets, and the flow duration. Each attribute have an independent threshold of mean (µ) and standard deviation (σ). The experimental results show that our method can classify an audio traffic, a video traffic and normal web traffic with a high precision and recall.