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Project Schedule

Project Schedule. Victor Gau, Yi-Hsien Wang, Trevor Bosaw, and Jenq-Neng Hwang 2007.12.14. Current P2P Identification Products. Many Companies have products to detect P2P traffic – usually to control or limit the amount of bandwidth these applications consume. Products can be: Port Based

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Project Schedule

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  1. Project Schedule Victor Gau, Yi-Hsien Wang, Trevor Bosaw, and Jenq-Neng Hwang 2007.12.14

  2. Current P2P Identification Products • Many Companies have products to detect P2P traffic – usually to control or limit the amount of bandwidth these applications consume. • Products can be: • Port Based • DPI Based • Flow Based

  3. Project: Build a Streaming Media Detector • To develop a Streaming Media (Both Unicast and P2P) traffic identifier and controller to enable networks to identify which flows contain Streaming Media. • As carriers upgrade networks, the ability to deliver internet video is becoming a key differentiator • Enabling theoretical 20-100mbps connectivity to the home is only important if consumer applications (Joost, Babelgum, Xunlei, ppstream, etc.) can be delivered with High QOS. • State-of-the-art Edge Routers can support flow based QOS • By elevating the QOS of flows needed for streaming media, the consumer experience can be enhanced • Related work • P2P Optimized Traffic Control • Riad Hartani and Joe Neil, Caspian Networks http://www.apricot.net/apricot2005/slides/KT8_2.pdf • Optimizing the Internet Quality of Service and Economics for the Digital Generation • Lawrence Roberts, Anagran Inc. http://www.itu.int/osg/spu/presentations/2006/worldtelecom2006/lroberts-itutelecom2006.pdf • Anagran flow router (FR-1000) http://www.anagran.com/

  4. Tasks • Classify network traffic • Identify Streaming Media traffic on a Flow by Flow Basis, using Port, DPI and Flow information. • Implement a Control Model to update and manage new signatures for both DPI and Flows • Analyze the accuracy of these methods on a real world network that heavily uses streaming media applications http://www.apricot.net/apricot2005/slides/KT8_2.pdf

  5. Consideration 1 • Which one should be used in this project? • Port/Signature-base identification • Accuracy of identifying known protocol • Ability to identify encrypted or new P2P protocol • Flow-based identification • Ability to identify encrypted or new P2P protocol • False positive rate (could annoy users) • We will design our own algorithms.

  6. Consideration 2 • What would be the performance metrics used in this project? • Accuracy of detection on a per flow basis • Further deep dives on accuracy – per byte, per 5-tuple, per packet. • Average delay on a per flow basis before identification is accomplished • Ease of update of Patterns and Flow Behavior • False Negatives – identification of background P2P file delivery as streaming media is very undesireable.

  7. Task 1Classify Network Traffic

  8. 1.1 Capture Packets • Construct a detailed list of current P2P and unicast streaming methods and clients/trackers/servers, as well as methods employed to defeat traffic shaping employed by these methods (for example azereus supports, encryption, proxying control traffic, etc.). • Capture packets generated by popular Internet applications. • P2P file Sharing and Streaming • eMule, FastTrack, BitTorrent, Gnutella, … • Joost, Babelgum, ppstream, Xunlei, • HTTP, FTP, mail, streaming, game, telnet, … • Instant Messaging Services • Skype, MSN, Yahoo! Messenger • Capture real world packets.

  9. Packet Information • Src [IP, port] • Dest [IP, port] • Type (TCP or UDP) • Size • Arrival time • Payload

  10. 1.2 Extract Characteristics • Flow duration • Packet counts of the flow • Average packet rate of the flow • Packet size distribution of the flow • Total bytes of the flow • Average transmission rate of the flow • …

  11. 1.3 Find Particular Flow Behavior • Find the behavior observed by others. • T. Karagiannis et al. • Both TCP and UDP (Ratio) • Ratio of the number of distinct ports versus number of distinct IPs • F. G. Chou • Packet size switching frequency per flow • Packet size standard deviation per flow • … • Observe the behavior by ourselves.

  12. 1.4 Design Program • Packet parser/analyzer • Flower identifier based on neural network • …

  13. 1.5 Test & Improve the Program • Use the identifier on real world data • Optimize its performance • Detection rate • False positive rate • Per Flow • Per Byte

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