CIS 6930: Workshop III Encounterbased Networks

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CIS 6930 Workshop IIIEncounter-based Networks

• Presenter Sapon Tanachaiwiwat
• stanachai_at_gmail.com
• Instructor Dr. Helmy
• 2/5/2007

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Agenda

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

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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-networkin
  g
• Large delay
• Low data rate
• High loss rate
• Basic assumptions of each node
• Persistent storage
• Willing to participate
• Limitation of Power
• Short Radio Range

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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 ?

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Examples of encounter-based networks

• Military tactical networks
• Disaster relief
• ZebraNet
• Interplanetary networks
• Rural village networks
• Underwater acoustic networks
• Other?

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http//www.cs.rice.edu/animesh/comp620/presentati
ons/JFP04_D.pdf
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Epidemic Routing
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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

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Encounter-based worm interaction
Susceptible
Predator
Prey
Prey and predators infection rate rely only on
encounter characteristics
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Analysis of Worm Interaction
SSusceptible IA Prey infected hosts IB
Predator infected hosts ß Contact rate
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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)
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Worm Propagation Based On Encounter Derived from
WLAN Trace
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Worm Interaction Based on Bluetooth i-Mote Traces
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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

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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?

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Example of Bluetooth map
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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.