1. Feng Li, Jie Wu, Avinash Srinivasan
Thwarting Blackhole Attacks in Disruption-Tolerant Networks using Encounter Tickets
Feng Li, Jie Wu, Avinash Srinivasan

2. Delay-Tolerant Networking
DTN stands for:
Delay Tolerant Networking
Disruption Tolerant Networking
Disconnection Tolerant Networking
EX: Buses on the road
Contact with other encounter buses
Contact with road side AP

3. DTN and Wireless Network
E2E Connectivity
Continues
Frequent Disconnection
Propagation Delay
Short
Long
Transmission Reliability
High
Low
Link Data Rate
Symmetric
Asymmetric
An end-to-end link may never exist in DTN

4. Opportunistic Networks
DTN Routing
Considering contact types
Knowledge Based
No knowledge
Flooding, Controlled Flooding, coding based
Partial Knowledge
Prediction using past history
Contact patterns
Delay Tolerant Networks (DTN)
Opportunistic Networks
Scheduled Networks
Predictable Networks

5. Who can help sending the message?
Suppose “A” want to send a message to “C”
Tell “C” his professor is very ANGRY!!!
I’ve met C
2 times a day B
5 times a day A D
Your professor is very ANGRY!!! C
4 times a week E

6. Metric-based DTN routing protocol
Using Past contact history to predict future contacts. C 5 D 10 E 3 B B 5 E 7 C 7 E 2 A D C B 3 D 2 E

7. MaxProp [J. Burgess, UMass, INFOCOM 2006] No knowledge, flooding based
MaxProp uses several mechanisms to route packets in a DTN:
At each TransOpp, packets are scheduled in an order based on:
Likelihood of delivery to destination
Packets with low hop-counts are prioritized.
When storage is low, packets are deleted in reverse order.
MaxProp reports delivery of packets globally, to clear buffers.
Hoplists reduce repeated propagation

8. However...... If a node provides forged numbers of contacts.
It is just like a “BLACKHOLE”
I’ve meet every one
99 times a day B A D C E

9. Main Contribution of this paper
Examining Blackhole attacks
Basic: forged metrics
Adv : Tailgating source or destination nodes
Introduce the notion of encounter ticket
Verifiable contact evidence
Proposing and encounter prediction system
Utilizing the time information record in encounter ticket to avoid the advanced Blackhole attacks.
Real trace driven simulation prove the proposed method. (UMass DiselNet)

10. Assumptions Each node has a fixed buffer for carrying messages
Transmission opportunities are limited both duration and bandwidth
Each node holds a unique ID and a public/private key pair
Each Packet has a delay requirement D
Nodes communicate using radio transmission
Becoming neighbors when they are within the range
Generate an encounter ticket

11. Encounter Ticket Generation
Each node has
A private key (RK)
A public key (PK)
Issued by the PKI (public key infrastructure)
Signed by the CA’s(certificate authority) private key
How to use these key for authentication
Exchange certificate if first meet
Using nodes public key
Authenticate it to the CA
The encounter record is encrypted by the destination nodes private key
Can’t be forged

12. The Process of Encounter Exchange

13. Encounter Ticket Generation
Hash function of concatenation (A,B,t)
Node A contact node B at time t
encryption using node A’s private key.
Packet ID

14. Are We Safe Now?
Could we prevent from Blackhole attack by using encounter tickets?
Encounter records can’t be forged.
Advanced Blackhole attack
Tailgating source or destination node B C A

15. Robust History Interpretation
Nodes need to interpret an attacker’s tailgating pattern.
Make an observation based on accumulated encounter history
Procedure
Generate evolving graph based on contact history
Make an observation based on the graph
Encounter prediction and decision making

16. Generate evolving graph based on contact history

17. Make an observation based on the graph
We want to know: Whether a path over time exists on which the packet can traverse within delay requirement “D“
A journey:
existing a path start at “ts” end at “td”
“td” < “D”

18. Four Possible Situation in Observation
Success +1
failure +1
overlap
success +1
overlap
failure +1
success +1

19. Encounter prediction and decision making
Success(existing a path) : α
Failure: β
Can’t decide which node is better without further evaluating
Destination C α 5 β B A
Destination C α 2 β 1 D

20. Deciding which node’s competence
Follow Dempster-Shafer theory
Mathematical theory of evidence based on
belief functions
plausible reasoning
combine separate pieces of information (evidence) to calculate the probability of an event.

21. Deciding which node’s competence - Follow Dempster-Shafer theory
Node A has a packet, has a proposition on node B’s competence.
X: All states under A’s consideration
P(X): All possible subset of X
According to Dempster-Shafer theory the next step is to find proper mass assignment of X in “P(X)”

22. Deciding which node’s competence - Follow Dempster-Shafer theory
Using Bayesian inference to connect observation results with the mass assignment for “P(X)”
statistical model in which evidence or observations are used to update or to newly infer the probability that a hypothesis is true.
Use Beta Distribution is used here in Bayesian inference

23. Deciding which node’s competence - Follow Dempster-Shafer theory
Initial: Beta(1,1)
When an observation is made
Success Beta(α+1, β)
Failure Beta(α, β +1)

24. Deciding which node’s competence - Follow Dempster-Shafer theory
The distribution of Beta(α, β) represent the delivery likelihood
The mass of “P(X)” should based on Beta(α, β)

25. Deciding which node’s competence - Follow Dempster-Shafer theory
Assign a proper mass of Node B
We know the number success and failure journeys
But we don’t know the uncertainty
Uncertainty u: (defined in their previous work)
u=1 when α = β = 1
Certainty = 1-u

26. Deciding which node’s competence - Follow Dempster-Shafer theory
The set {B is competent}

27. Deciding which node’s competence - Follow Dempster-Shafer theory
Decision Rule:
A node should select and forward the packets to the most competent forwarders with sufficient contact evidences.
Substituting the delivery likelihood matric by

28. An example t1 ~ t4, success t4 ~ t7, failure t7 ~ t9, failure
A generates a packet for G with D = 3 at time t9 and c = 1
B is an attacker
Observation
t1 ~ t4, success
t4 ~ t7, failure
t7 ~ t9, failure
B: α =2, β =3 ,u=0.48, b = 0.208
C: α =3, β =2 ,u=0.48, b = 0.312

29. Simulation and Analysis
Setup
trace-driven simulation
Real trace from UmassDieselNet
33 Nodes
Assign at most 5 attackers can exist

30. Delivery rate with\without tickets

31. Packet attract with\without tickets

32. Delivery Rate in Different Attacks

33. Conclusion Strength Weakness
Proposing an encounter ticket scheme to secure the evidence of contacts.
Using Dempster-Shafer theory to decied a proper node’s competence
Weakness
Some errors on the paper (weird)
Message overhead
to the certificate authority
Encoding and decoding complexity
When there are lots of nodes