1 CIS 6930: Workshop III Encounter-based Networks Presenter: Sapon Tanachaiwiwat 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

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 Predator Prey Susceptible Prey and predator’s infection rate rely only on encounter characteristics

10 Analysis of Worm Interaction S=Susceptible I A = Prey infected hosts I B = Predator infected hosts β = Contact rate

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

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: 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.