1. fine-grained reputation-based routing in wireless ad hoc networks
Alma Cemerlic
Department of Computer Science and Engineering
Master Thesis Defense

2. outline Introduction Related work Our methodology
Wireless ad hoc network
Motivation of the study
Definition of terms
Related work
Our methodology
Fine-grained reputation system
Integration with a wireless routing protocol
Simulation and evaluation
Conclusion

3. wireless ad hoc network characteristics (1)
Ad hoc mode
No base stations
Nodes can only transmit to other nodes within link coverage
Nodes organize themselves into a network – route among themselves

4. wireless ad hoc network characteristics (2)
No infrastructure support
Limited resources (power, memory, and processing)
Easily eavesdropped
Naïve trust model
Examples
Hostile environments like battlefields or rescue operations

5. motivation of the study
Most legacy models of wireless ad hoc routing do not consider security and use hop count as a measure of path cost.
We explore reliability of each node to improve the quality of service and security.
An approach to routing which incorporates security (reputation) of nodes into legacy routing metrics

6. routing based on fine-grained reputation system
Basic idea:
Route packets through nodes with high reputation values
Protect network traffic from misbehaving nodes
Improve effective throughput
Minimize interaction with misbehaving nodes
Solution
Evaluate reputation of each node
Integrate reputation metric into route selection

7. definition of terms and assumptions (1)
Neighbors – communication range of two nodes overlaps.
Neighborhood – all neighbors
Behavior – the way that a node handles packet forwarding, i.e. correctly forwards, alters, injects, etc.
Reputation – evaluated through observation of a neighbors’ behavior.
Report – reputation information periodically exchanged among neighbor nodes.
Trust – indicates whether reports coming from a particular node can be considered trustworthy.

8. definition of terms and assumptions (2)
Bidirectional communication on every link
Many wireless Medium Access Control (MAC) layer protocols require bidirectional communication for reliable transmission
Network interfaces on the nodes support promiscuous mode operation
If a node A is within range of node B, it can overhear all communications to and from B

9. adversary models
Adversaries are nodes that misbehave in such ways that they degrade integrity and availability of the network
Misbehaviors include:
Packet alternation and injection done by malicious nodes when they are supposed to forward packets for other nodes
Packet dropping done by selfish nodes when they are supposed to forward packets for other nodes

10. related work
Use forwarding behavior of a node to estimate its reliability.
Nodes categorized as good or bad (Beta Reputation System)
Distinguishing between selfish and malicious behavior is not possible
CONFIDANT (Buchegger 2002a), CORE (Michiardi 2002a), SORI (Wang 2004b), SAFE (Rebahi 2005)
Virtual currency in wireless routing
Packet Purse Model
Packet Trade Model
Nugglets (Buttyan 2001), (Wang 2004a), (Jakobsson 2003)

11. fine-grained reputation system
“Self-organized system”
Decentralized, cooperation cannot be guaranteed
Reputation systems – “soft security”
Stimulate ethical behavior and integrity of members in collaborative environments; recognize and sanction intolerable behavior, reward obedient members
Each node observes behavior and evaluates reputation of its neighbors
Neighbors exchange reputation information
Reputation is used to select the most reliable and secure path from source to destination

12. fine-grained reputation classification based on node behavior
Friendly
Selfish
Malicious
Friendly Nodes
Correctly forward packets; expected behavior
Selfish Nodes
Drop packets; expect other nodes to route their packets;
Physical properties (battery, overload), attempt to save resources, random failure
Harm availability of the network
Malicious Nodes
Misroute, alter, or inject packets;
Harm integrity of packets
If behavior changes, node’s reputation changes correspondingly.

13. fine-grained reputation evaluation
Total Reputation is the combination of
first-hand and
second-hand reputation
First-hand reputation
from direct observation of neighbors’ behavior
Second-hand reputation
from neighbors’ reports

14. first-hand reputation (1)
Each node observes the forwarding behavior of its neighbors:
Packets may be correctly forwarded, dropped, maliciously modified.
A node’s behavior follows the Dirichlet distribution Dir(α), where α = (α1, … , αn)
Conjugate prior to the Multinomial distribution
The probability that each independent trial result is exactly one of some fixed finite number n of possible outcomes with probabilities p1,…, pn
Bayes Theorem
Allows us to incorporate new observation into prior knowledge and obtain posterior probability of node’s behavior.

15. first-hand reputation (2)
Combine the Dirichlet distribution and Bayes Theorem
Given the prior the posterior distribution is calculated as
Starting with the initial state of the prior distribution, the parameters are updated when new data D is available
N represents instances of the new data
First-hand reputation value at a time t is equal to the expectation value of Dir(α)

16. first-hand reputation - example
Window
Number of observed packets α1 α2 α3 1 50 40 10 2 30 15 5 3 4 35
Total number of observed packets in windows 0-5
250
185
Expectation of Xi
---
(185) / (250) = 0.74
(50) / (250) = 0.2
(15) / (250) =0.06
Behaviors of a node change from friendly to selfish and malicious.

17. second-hand reputation
Collaborative monitoring : exchange first-hand reputation with the neighbors
Nodes are required to periodically broadcast
Deviation test – to detect false reports
Increase or decrease trust?
Incorporate the report into the total reputation value?

18. trust Indicates how trustworthy the neighbor’s reports are
The same Bayesian approach as for the fine-grained reputation system
Only two possible instances of behavior: trustworthy and not trustworthy
Use the Beta distribution, conjugate prior to the Binomial distribution
TAB ~ Beta(γ, δ), where γ = trustworthy, δ = not trustworthy.
Updated when the results of the deviation test are available
Trust value is calculated as:

19. total reputation value
Merge first-hand and second-hand reputation information
Second-hand reputation is discounted by the factor ω (trust value) expressing the disbelief in the accuracy of the report

20. total reputation value
5. Merge first-hand and second-hand reputation values to derive total reputation value RAB
1. Calculate first-hand reputation FAB based on observations.
Reputation Calculation
Reputation Report FCB
3. Deviation Test and Trust Threshold Test
2. Second-hand reputation reports.
4. Depending on the outcome of the deviation test and the trust threshold test, decide whether to accept the second-hand reports.
Reputation Report FDB
Observation Windows
Trust Table
6. Update trust value for the reporters C and D: ωAC and ωAD. depending on the outcome of the deviation test.

21. moving window mechanism
Two ways to update first-hand information
Based on all observations
Based on the most recent observations
Reduces computational complexity
Early detection of changes in behavior
Possibility of redemption over time for misbehaving nodes
To calculate first-hand information:
We divide historic information into time intervals of equal size and considered only a limited number of the most recent intervals

22. moving window mechanism - example
Number of observed packets α1 α2 α3 1 50 2 3 40 10 4 5
Windows 0-5
Total number of observed packets in windows 0-5
250
220 30
Mode of Xi
---
(220) / (250) = 0.88
(0) / (250) = 0
(30) / (250) = 0.12
Windows 3-5
Total number of observed packets in windows 3-5
150
120
(120) / (150) = 0.8
(0)/ (150) = 0
(30) / (150) = 0.2
Reputation based on recent history better reflects changes in behavior

23. integrating reputation into ad hoc routing (1)
Our fine-grained system classifies wireless nodes as friendly, selfish, or malicious.
Malicious nodes cause more damage than selfish nodes.
Reputation of each node is denoted by a vector containing friendly, selfish and malicious parameters (f, s, and m).
Each node consider reputation of neighbor nodes to route packets.
<0.8, 0.2, 0.0> B A D C
<0.9, 0.1, 0.1>

24. integrating reputation into ad hoc routing (2)
Cost function converts a reputation vector <f,s,m> into a cost value used to determine most reliable path:
where
Malicious nodes cause more damage than selfish nodes – therefore b<c
Routing when there is no distinction between selfish and malicious behavior:
Fine-grained reputation system
Beta reputation system

25. integrating reputation into ad hoc routing (3)
Moving window size
Determines the sensitivity of the system
Allows gradual redemption

26. simulation and evaluation (1)
50 nodes are placed on 800m by 800m stage
Data packets emission rate is 50 packets/second
Low mobility – static
All nodes are connected
Random sender-destination pairs
Adobe Flash CS3, in AS3.0
Graphical and interactive capabilities
Support for OO programming
Platform independence and portability

27. simulation and evaluation (2)
Evaluation Metrics
Effective throughput
Percentage of packets manipulated by malicious nodes
Connectivity of the network
Compare fine-grained reputation system with
Beta system – classify nodes into friendly or selfish
Beta system – classify nodes into friendly or malicious

28. simulation - scenarios
Same scenarios tested on all three systems
Selfish nodes – drop approx. 20% packets
Malicious nodes – inject approx. 20% packets
Number of misbehaving nodes varies form 4 – 40% of the population (add a reference about 40%)

29. results (1) Effective throughput
The ratio of legitimate traffic and total traffic, where legitimate means that the traffic was not produced as a consequence of the malicious activity
Our system shows highest effective throughput over beta systems

30. results (2) Percentage of packets manipulated by malicious nodes
Our system shows lowest percentage of packets manipulated by malicious nodes.

31. results (3)
Connectivity: average cost for nodes to communicate with each other.
Our system shows higher connectivity over beta (malicious) system
Our system show slight lower connectivity over beta (selfish) system. But the increased connectivity in latter system is used to carry malicious traffic, which is indicated by lower effective throughput in beta (selfish system).

32. results - conclusion
Random communication, 10% misbehaving on every 50 packets forwarded
Effective throughput
Beta malicious vs. <f,s,m> 2.84% increase
Beta selfish vs. <f,s,m> 2.5% increase
Percentage of malicious packets
Beta malicious vs. <f,s,m> 2.76% decrease
Beta selfish vs. <f,s,m> 2.49% decrease
Connectivity (over 40% set)
Beta malicious vs. <f,s,m> 9.55% increase
Beta selfish vs. <f,s,m> 10.56% decrease

33. Fine-grained reputation system
Beta (selfish) reputation system
Beta (malicious) reputation system
Effective Throughput
HIGH
LOW
MEDIUM
Percentage of packets manipulated by malicious nodes
Connectivity

34. CONTRIBUTION
Proposed a novel solution to evaluating and managing reputation and trust in P2P environment.
Integrated reputation management into wireless ad hoc routing.
Defended wireless ad hoc network against misbehaving nodes.
Implemented and evaluated fine-grained reputation system in application of wireless ad hoc routing
Our system performs better than the Beta reputation systems:
higher effective throughput and
less packets manipulated by malicious nodes

35. references
Buchegger, S., Le Boudec, J. (2002). Performance analysis of the CONFIDANT protocol: Cooperation Of Nodes – Fairness In Dynamic Ad-hoc NeTworks. IEEE/ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHOC), Lausanne, Switzerland.
Buchegger, S., Mundinger, J., Le Boudec, J. (2008). Reputation systems for self-organized networks: lessons learned. IEEE Technology and Society Magazine, Special Issue on Limits of Cooperation in Wireless Communications.
Buttyan, L., Hubaux, J. (2001). Nuglets: a virtual currency to stimulate cooperation in self-organized ad hoc networks. Lausanne, Switzerland, Institute for Computer Communications and Applications, Swiss Federal Institute of Technology.
Jakobsson, M., Hubaux, J., Buttyan, L. (2003). A micro-payment scheme encouraging collaboration in multi-hop cellular networks. Proceedings of Financial Crypto, La Guadeloupe.
Michiardi, P., Molva, R. (2002a). CORE: A COllaborative Reputation mEchanism to enforce node cooperation in mobile ad hoc networks. The IFIP TC6/TC11 Sixth Joint Working Conference on Communications and Multimedia Security: Advanced Communications and Multimedia Security.
Rebahi, Y., Mujica, V. E., Simons, C., Sisalem, D. (2005). SAFE: Securing pAcket Forwarding in ad hoc nEtworks. 5th Workshop on Applications and Services in Wireless Networks. Paris, France.
Wang, W., Li, X., Wang, Y. (2004a). Ad hoc-VCG: a truthful and cost-efficient routing protocol for mobile ad hoc networks with selfish agents. MobiCom '04, San Diego (CA), ACM.
Wang, Y., Giruka, V. C., Singhal, M. (2004b). A fair distributed solution for selfish nodes problem in wireless ad hoc networks. Ad-Hoc, Mobile, and Wireless Networks, Springer Berlin / Heidelberg. 3158:
Yang, L., Kizza, J.M., Cemerlic, A., Liu, F. (2007). Fine-grained reputation-based routing in wireles ad hoc networks. IEEE International Conference on Intelligence and Security Informatics, New Brunswick, NY, IEEE.
Yang, L., Novobilski, A, Ege, R. (2008). Trust-based usage control in collaborative environment. The International Journal of Information Security and Privacy 2(2).

36. questions?