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CIS 6930: Workshop III Encounter-based Networks

CIS 6930: Workshop III Encounter-based Networks. Presenter: Sapon Tanachaiwiwat stanachai@gmail.com Instructor: Dr. Helmy 2/5/2007. Agenda. Introduction Motivation Examples of Encounter based networking Encounter-based worm interactions Experiment for our class Reference. Introduction.

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CIS 6930: Workshop III Encounter-based Networks

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  1. CIS 6930: Workshop IIIEncounter-based Networks Presenter: Sapon Tanachaiwiwat stanachai@gmail.com Instructor: Dr. Helmy 2/5/2007

  2. Agenda • Introduction • Motivation • Examples of Encounter based networking • Encounter-based worm interactions • Experiment for our class • Reference

  3. Introduction • What is Encounter-based networking • Networking relying on encounter or relationships between nodes (Social networking) • Wireless ad hoc networks • Discontinuous path (Intermittent connection) • Store-and-forward (Bundles) • Similar to delay-and-disruption-tolerant-networking • Large delay • Low data rate • High loss rate • Basic assumptions of each node • Persistent storage • Willing to participate • Limitation of Power • Short Radio Range

  4. Motivation • Why we need encounter-based networks • Reasons? • What we can learn from Experiment 1 and 2 • Wireless LAN Coverage on Campus is good for any where and any time computing? • How can you analyze of the potential of encounter-based networking? • Step 1: Look where the holes on campus? • Step 2: Analyze the encounter characteristic based on WLAN • Step 3: Do Experiment number 3 • Step 4: ?

  5. Examples of encounter-based networks • Military tactical networks • Disaster relief • ZebraNet • Interplanetary networks • Rural village networks • Underwater acoustic networks • Other?

  6. http://www.cs.rice.edu/~animesh/comp620/presentations/JFP04_D.pdfhttp://www.cs.rice.edu/~animesh/comp620/presentations/JFP04_D.pdf

  7. Epidemic Routing

  8. Encounter-based worms • Future direction on worm attacks!! (Cabir, ComWar) • Rely on encounter pattern/relationships between users. • Close to flooding, i.e. Epidemic routing. • Propagate via Bluetooth connection (10-meter range) • Question: How can we alleviate this problem? • Traditional prevention at gateway such as firewall not effective against fully distributed attacks • Disconnected networks  No centralized update • Inspired by War of the Worms: CodeGreen worms launched to terminate CodeRed worms • Approach: Deploy automated generated predator worm to terminate prey worm  worm interaction

  9. Encounter-based worm interaction Susceptible Predator Prey Prey and predator’s infection rate rely only on encounter characteristics

  10. Analysis of Worm Interaction S=Susceptible IA= Prey infected hosts IB = Predator infected hosts β = Contact rate

  11. Simulation Results Encounter level simulation with 1000 mobile nodes having uniform encounter Reaction time Reaction time Mathematical Model Simulation Based on aggressive one-sided interaction encounter rate = contact rate Closely estimate the infectives when varying reaction times (off 3.8%)

  12. Worm Propagation Based On Encounter Derived from WLAN Trace

  13. Worm Interaction Based on Bluetooth i-Mote Traces

  14. Experiment Setup • Goal: To answer the following questions • Is the UF campus the good target for worm propagation, given that it propagates via Bluetooth? • If so, what places are most vulnerable? • If you want to stop the propagation with other worm, how can you do it effectively? • Equipments: iPAQs, your laptops, your strategies • Software: Modified Bluechat, Bluetooth Explorer,Netstumbler, AirSnort, etc. • Trace format of Modified Bluechat: • Name of device (brand) [MAC Address] Month/Date/Year Hour/Minute/Second

  15. Experiment • Bluetooth device discovery • Distribution of Bluetooth devices that you encounter during the day • E.g. Type of devices such as cell phone or lap top, brand of such devices such as Nokia, Motorola, etc. • Bluetooth game Design the strategies for • Largest of encounter rate per day • Largest number of unique devices • Largest number of stable devices (long-duration encounters) • Different roles between teams e.g. Cops and Cons • Bluetooth and WLAN relationships • Can you derive the correlation between them?

  16. Example of Bluetooth map

  17. Reference • E. Anderson, K. Eustice, S. Markstrum, M. Hansen, P. L. Reiher , “Mobile Contagion: Simulation of Infection and Defense” PADS 2005: 80-87 • S. Capkun, J. P. Hubaux, and L. Buttyan "Mobility Helps Security in Ad Hoc Networks" Fourth ACM Symposium on Mobile Networking and Computing (MobiHoc), June 2003 • F. Castaneda, E.C. Sezer, J. Xu, “WORM vs. WORM: preliminary study of an active counter-attack mechanism”, ACM workshop on Rapid malcode, 2004 • A. Chaintreau, P. Hui, J. Crowcroft, C. Diot, R. Gass and J. Scott, “Impact of Human Mobility on the Design of Opportunistic Forwarding Algorithms” IEEE INFOCOM, April 2006 • W. Hsu, A. Helmy, "On Nodal Encounter Patterns in Wireless LAN Traces", The 2nd IEEE Int.l Workshop on Wireless Network Measurement (WiNMee), April 2006 • S.Tanachaiwiwat, A. Helmy, "Encounter-based Worms: Analysis and Defense", IEEE Conference on Sensor and Ad Hoc Communications and Networks (SECON) 2006 Poster/Demo Session, VA, September 2006 • A. Vahdat and D. Becker. Epidemic routing for partially connected ad hoc networks. Technical Report CS-2000.

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