1. PROVEST: Provenance-based Trust Model for Delay Tolerant Networks
Jin-Hee Cho, Ing-Ray Chen

2. 1. Introduction
Introduction | Method | Evaluation

3. What is Disruption Tolerant Networks (DTN)
There is no guarantee of end-to-end connectivity, thus causing high delay or disruption due to
inherent characteristics
or intentionally misbehaving nodes
Examples: smart environments, habitat monitoring, and vehicular ad- hoc networks

4. What do we expect DTN behaves
Achieve accurate peer-to-peer trust assessment
Maximize the delivery of correct messages received by destination nodes
Minimizing message delay and communication cost under resource- constrained network environments

5. A challenge
Nodes are sparsely scattered in DTN and do not often encounter each other
The lack of direct interaction experience in DTN environments hinders continuous evidence collection and can result in incorrect trust estimation, leading to poor application performance.

6. Provenance “lineage” or “pedigree,”
Describing the origins of a piece of data and how the data achieves the current state
Can be think as another communication method, an indirect method

7. Challenge of using provenance
It must defend against attackers who may modify or drop messages including provenance information or disseminate fake information

8. Network model
Information: source nodes (SNs) ==> destination node (DNs)
“store-and-forward” technique: a node carries messages until it encounters a message carrier (MC)

9. 2. Method
Introduction | Method | Evaluation

10. PI (SN) ==> destination node (DN)
Pi;k representing the PI provided by i with its direct trust opinion towards the previous MC =
Oi;k(t) is I’s direct trust opinion towards the
attack behaviors (i.e., ID, fake recommendation, and message modification attacks), and
remaining energy level of k

11. Peer-to-peer trust estimation
r: amount of positive evidence
s: amount of negative evidence
initiated with r = 1 and s = 1
Derived from either:
direct evidence based on observations
or indirect evidence (PI)

12. Uncertainty of evidence - u
Direct evidence: can be failed due to unreliable link or short contact time
Indirect evidence: false evidence will not be used.
Three scores: r, s, u
Accumulated evidence from the past and new evidence
How to aggregate?

13. Trust Aggregation PROVEST-Pessimistic PROVEST-Optimistic
PROVEST-Realistic
PROVEST-Hybrid
The value of each trust dimension is aggregated based on accumulated evidence from the past and the new evidence

14. PROVEST-Pessimistic
Treats uncertain evidence as negative evidence based on the nature of trusting less under no correct evidence available
i to refer to a trustor (i.e., evaluator) and j to refer to a trustee (i.e., evaluatee).

15. PROVEST-Optimistic
Treats uncertainty as credits based on the nature of trusting more

16. PROVEST-Realistic
Only relies on evidence available by ignoring the uncertain evidence
If no new evidence is available, it does not update trust

17. PROVEST-Hybrid Leveraging the three schemes above
It determines how to deal with uncertain evidence based on historical patterns of the amount of evidence

18. Trust Dimensions Availability Direct trust Integrity Indirect trust ×
Competence
Direct trust
Indirect trust ×

19. Direct availability trust
Direct availability trust is measured by whether a node is available to serve requests by exchanging a simple message to ensure connectivity.
Replies: (1,0,0)
No replies: (0,1,0)

20. Direct integrity trust
Direct integrity trust is measured based on whether a node exhibits three attack behaviors: identity attack, fake recommendation attack, and message modification attack
Each exhibiting attack behavior is counted as evidence
r+s+u=3

21. Direct competence trust
Energy status + cooperativeness behavior
r+s+u=2

22. Indirect availability trust
Positive (1,0,0) if
(1) node j’s ID is enclosed in j’s PI;
(2) node j’s ID is authentic by ensuring that j’s ID inserted by j in j’s PI matches with j’s ID inserted by j’s next MC in the next PI;
(3) both j’s previous MC and j have a trust value above the minimum trust threshold based on i’s evaluation

23. Indirect integrity trust
Also three pieces of evidence: identity attack, fake recommendation attack, and message modification attack
If PI is inserted:
j’s next MC’s trust value (in the last trust update) > threshold
For each evidence
r+s+u=3

24. Indirect competence trust
Also energy status + cooperativeness behavior
If PI is inserted:
j’s next MC’s trust value (in the last trust update) > threshold
For each evidence
r+s+u=2

25. 3. Evaluation
Introduction | Method | Evaluation

26. Metrics - Trust Bias Time-averaged difference between:
trust of node j evaluated by node i
and objective trust of node j evaluated by all encountered nodes based on direct observations with no detection errors.
ground truth
Trust value of node j on property X evaluated by node i at time t

27. Metrics - Mission message correctness
Fraction of the number of packets received by DNs correctly over the total number of messages transmitted
by SNs during LT.
I is a set of messages received by DNs and the k nodes are intermediate MCs delivering message m.
m: message
K is a set of all intermediate MCs involved in delivering each message m.

28. Metrics - Message delay
Dm is the delay (sec.) occurred for message m to be delivered to the DN.
I is a set of messages sent by SNs to DNs

29. Metrics - Communication cost
Ce(t): number of messages for a node to deal with trust evaluation
Cd(t): number of messages for a node to deal with message delivery
LT: entire mission lifetime

30. Experimental Setup 20 nodes Communication range: 100m
Speed: uniform distribution with the range of [1,15]
Packet forwarding probability: Pf
Packet dropping probability: 1-Pf
Number of SN-DN pairs: 20
And so on

31. STOCHASTIC PETRI NETS

32. Trust Bias of PROVEST

33. Performance of PROVEST

34. Comparing methods Trust-based Non-trust-based
PROVEST (and its variants)
Encounter-based
Iterative Trust Reputation Mechanism (ITRM)
Non-trust-based
epidemic (e.g., flooding)
ProPHET (e.g., connectivity-based delivery prediction)

35. Comparing Results
We found that the provenance-based approach (i.e., PROVEST-Hybrid) significantly reduces the communication cost while maintaining a high correct message delivery ratio, compared to Epidemic, ITRM, Encounter-based, and PRoPHET

36. Conclusion Direct method Intrinsic properties of DTN: Two methods
Indirect method (PI)
1. Sparse nodes
2. Various attacks
Introduce u
Four variants
3. no guarantee connectivity
4. Inherent problems
Three dimensions

37. Thank you