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Cooperative and Reliable Packet-Forwarding on Top of AODV

Cooperative and Reliable Packet-Forwarding on Top of AODV. Tal Anker, Danny Dolev, Bracha Hod The Hebrew University of Jerusalem. Outline. Introduction Background: Trust and Reputation Motivation and Contribution Solution Highlight Simulation Framework and Results Conclusions.

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Cooperative and Reliable Packet-Forwarding on Top of AODV

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  1. Cooperative and Reliable Packet-Forwarding on Top of AODV Tal Anker, Danny Dolev, Bracha Hod The Hebrew University of Jerusalem

  2. Outline • Introduction • Background: Trust and Reputation • Motivation and Contribution • Solution Highlight • Simulation Framework and Results • Conclusions

  3. Introduction • Mobile ad-hoc networks are vulnerable to many attacks by selfish or malicious nodes • The adversary model – data packet dropping • Black hole node drops all the data packets • Gray hole node adversary selectively drops some data packets but not other • Other malicious attacks are beyond the scope of this work • Recent approaches • Watchdog and Pathrater [Marti, Giuli, Lai and Baker 2000] • CONFIDANT [Buchegger and Le Boudec 2002] • CORE [Michiardi and Molva 2002] • OCEAN [Bansal and Baker 2003]

  4. Trust and Reputation • Trust and reputation use past behavior to predict current behavior • Trust is based on direct experience • Reputation is derived from both direct and indirect information • In a reputation system, nodes compute and advertise rating values • Direct information and indirect information can also be referred as first-hand observation and second-hand observation, respectively

  5. Motivation and Contribution • AODV is one of the leading routing protocol for MANET • Most solutions have focus on DSR • DSR nodes access greater amount of information which enable their more rapid recovery from misbehaving nodes • Full-path routing • Multiple paths • AODV is much more scalable • Our work is the first reputation system on top of AODV

  6. First-hand Observations • Neighbors’ monitoring to detect misbehavior using passive acknowledgment (a.k.a. watchdog) • Each node overhears its neighbors and records their positive and negative actions • Inherent weaknesses, such as collisions • Weaknesses in AODV • Need for “next hop” information • Mistakes in several situations, e.g., dropping during local repair • Associated with less overhead and delay

  7. Second-hand Observations • Reputation system based on the Beta distribution function • Direct rating together with positive and negative actions are derived from the direct observations • Rating exchange between neighbors periodically • Total rating is an incorporation of the direct and indirect rating. It is used to classify the nodes • Trust is used to defend against liars • Local model as a result of MANET constrains

  8. Misbehavior Reaction • Nodes’ classification • Total rating value with positive and negative actions • Positive actions estimate load • Negative actions estimate misbehavior • Two nodes with the same total rating, but with different history are classified differently • Path selection • Greedy selection of the next hop • Path maintenance for partial dropping • Misbehaving nodes’ punishment • Second chance when the rating is faded

  9. Simulation Model • Simulation in GloMoSim • Standard parameters of the channel and radio model • IEEE 802.11 as the medium access protocol • Nodes are placed randomly in the area • Area of 1000x1000, 1500x1500 and 5000x5000 meters for 50, 100 and 500 nodes respectively • Movement by Random waypoint model • Speed range of 5-20 m/s • Pause time range of 0-500s • Data packets transmission at CBR on routes above 1-hop length

  10. Throughput of Well-behaving Nodes 50 Nodes 100 Nodes 15 Sources, 15 Black-holes 20 Sources, 30 Black-holes

  11. Punishment of Misbehaving Nodes Data Packets Transmitted Data Packets for by Misbehaving Nodes Misbehaving Nodes That were not Transmitted 50 Nodes, 15 Sources, 15 Black-holes

  12. Partial Dropping (Gray holes) Data Packets Dropped Dropping percentage of 50% Different Dropping (32% of the total rating) Percentages 50 Nodes, 15 Sources, 15 Gray-holes

  13. Robustness against Advanced Liars Data Packets Received False Positives 50 Nodes, 15 Sources, 10 Black-holes

  14. Scalability over AODV Throughput Data Packets Dropped 500 Nodes, 250 static and the remainder walk on speed of 5-10 m/s. 30 Sources, 50 black holes.

  15. Conclusions • A reputation system on top of AODV is effective for both partial and complete dropping • The reputation system remained robust against advanced liars, when a majority of the nodes are trustworthy • In large networks, it is better to rely on self-observations because the network conditions have greater effect than the reputation system benefits

  16. Backup

  17. The Beta Distribution Function

  18. Direct Rating • Direct rating of a node j by its neighbor i

  19. Total Rating past actions current actions indirect info.

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