1. Integrated Social and Quality of Service Trust Management of Mobile Groups in Ad Hoc Networks Ing-Ray Chen, Jia Guo, Fenye Bao, Jin-Hee Cho Communications Surveys & Tutorials, IEEE 13.4 (2011): 562-583 Speaker: Liang Zhao

2. Outline 1.Background 2.Trust Management Protocol 3.Model-based Evaluation Technique. 4.Evaluation Results 5.Conclusion and Future Works

3. Background Trust Management: A mobile ad hoc network (MANET), sometimes called a mobile mesh network, is a self- configuring network of mobile devices connected by wireless links. Mobile Ad Hoc Network (MANETs): 1. Abstract system that processes symbolic representations of social trust 2. Aid automated decision-making process.

4. Problems in MANET trust Management 1. Traditional QoS Trust Metrics did not consider Social Trust as metric. 2. Existing trust Metrics lack good aggregation parameter settings. 3. Effectiveness of Trust Management Protocol is hard to be evaluated due to difficulty of getting labels based on ground truth.

5. Contributions 1. Consider social metrics: i.e. intimacy (social ties) and honesty (healthiness). 2. Identify best trust aggregation parameter settings for each trust metric. 3. For validating proposed trust management protocol, a novel model-based evaluation technique is leveraged to generate ground truth. SQTrust Model-based Evaluation

6. SQTrust: A New Trust Management Protocol

7. SQTrust: Preliminary 1. Social Ties (Intimacy) 2. Honesty (Healthiness) measure the social trust level of a node as these social properties are considered critical for trustworthy mission execution Most important metrics to measure the QoS trust level of a node 3. Competence (Energy) 4. Protocol compliance (Cooperativeness) Trust Metrics (trust components) taken into account:

8. SQTrust: A New Trust Management Protocol What is it for? How to infer it? By collecting all the observations from other nodes For inferring the trust belief of each node in the network Trust Observations of node j by node i

9. SQTrust: A New Trust Management Protocol Consider the following trust metrics (namely trust components): 1. Intimacy 2. Healthiness 3. Energy 4. Cooperativeness Social Metrics Qos Metrics Weight of each trust component Each trust component How to determine them?

10. SQTrust: A New Trust Management Protocol Consider both direct trust and indirect trust. How to determine them? Directly collected by Node i toward node j. Indirect evidences given to node i by a subset of 1-hop neighbors selected.

11. SQTrust: Direct Trust How to infer the Direct Trust of a node? Well, it depends. - If Node i is 1-hop neighbor of node j -Otherwise, exponential trust decay over time.

12. SQTrust: indirect trust Inferring Indirect Trust is a little more complex. 1. Selection of Subset of 1-hop neighbors. Threshold-based filtering: only consider trustworthy recommenders Relevance-based trust: only consider trustworthy nodes under current trust component <threshold compromised Low trust in healthiness

13. Trust decay over spaceTrust decay over time SQTrust: Indirect Trust 2.Calculation of indirect trust -If there is at least one qualified neighbor: -Otherwise, Node i’s trust in node m Node m’s trust in node j

14. Model-based Evaluation

15. Schema: 1. Leverage SPN to build a semi-Markov chain to generate the nodes’ status. 2. Reward Assignment for each status. 3. Objective trust calculation.

16. a semi-Markov chain for node status Node Status is of 5 status representations: 1. Location.(int) 2. Member.(boolean) 3. Energy.(boolean) 4. Healthiness.(boolean) 5. Cooperativeness.(boolean) trust components To tell the position proximity of nodes

17. Location Is fired when node moves to another region. What is it for? 1. Enable the underlying semi-Markov model to give the probability that each node is in a certain region. 2. Thus to tell whether a node is 1-hop neighbor of another. # of tokens depends on the region a node moving into Initial speed Wireless radio range

18. Intimacy Consider both direct trust and indirect trust. For direct trust: 1. utilize location probability of a node to infer if nodes i and j are 1-hop neighbors. Based on the probability node i and node j are in the same region.

19. Energy To get the probability of current energy level of a node. initialize different value to different nodes to emphasize the heterogeneity. -lower when node becomes uncooperative to save energy -higher when being compromised Initial # of tokens: depends on the initial value Transition rate:

20. Healthiness (CN) A node is compromised when T_COMPRO fires Transition rate: _ A token goes to CN when a node is compromised Then, either of below can happen: 1. Good-mouth a bad node with a high trust recommendation 2. Bad-mouth a good node with a low trust recommendation

21. Cooperativeness (UNCOOP) A token goes to UNCOOP when a node is uncooperative. depends on energy, mission difficulty and neighborhood uncooperativeness degree: Lower energy, less cooperative Harder the mission, more cooperative Less cooperative 1-hop neighbors, more cooperative Group communication interval

22. Reward Assignment for each status

23. Objective Trust Calculation (1) For healthiness, energy or cooperativeness: (2) For intimacy: by aggregating all the trust components calculated as: Probability the system is at status s at time t

24. Evaluation Results

25. Parameter Settings Total 150 nodes, initially all are not compromised in MANETs. Initially all are trustworthy Based on ns3 simulation

26. Evaluation Results Overall trust values from subjective trust v.s. objective trust The value around 85% is the best trade-off

27. Conclusions and Future Works 1. Purpose of this paper: A protocol which minimizes the trust bias and maximize application performance. 2. Applicability: Based on the optimal protocol settings we get, we apply it for dynamic trust management with considering the environment changes. Future Works: Consider more sophisticated attacker behaviors, i.e. opportunistic, random and insidious attacks.

28. Thanks