Fenye Bao , Ing -Ray Chen, Moonjeong Chang Presented by: Changlai Du Feb 27, 2014

1. Hierarchical Trust Management for Wireless Sensor Networks and its Applications to Trust-Based Routing and Intrusion Detection FenyeBao, Ing-Ray Chen, Moonjeong Chang Presented by: Changlai Du Feb 27, 2014

2. Contents • Introduction • System Model • Hierarchical Trust Management Protocol • Performance Model • Trust Evaluation Results • Trust-based Geographic Routing • Trust-based Intrusion Detection • Conclusion

3. Introduction • Propose a cluster-based hierarchical trust management protocol for WSNs. • Utilize both Quality of Service (QoS) and Social Networking attributes to model the behaviors of nodes to determine their reliability. • Highly scalable due to being a cluster-based model. • Apply the protocol to trust-based geographic routing and trust-based intrusion detection.

4. Wireless Sensor Network • A Wireless Sensor Network (WSN) refers to a distributed network of autonomous sensors, each operating independently for the greater good of the network. • A WSN is inherently unstable due to the independence of the Sensor Nodes (SN) and their different operating characteristics, including malicious and selfish activity. • The WSN must take input from its SNs, evaluate their input, and determine the overall picture for what is happening across its network.

5. Sensor Node • A SN monitors physical or environmental conditions, such as temperature, sound, vibration, pressure, motion, or pollutants. • A SN is can transmit, or forward information through multi-hop routing. • SNs have very limited resources: • Energy • Memory • Computational Power • May be compromised and perform to malicious attacks.

6. Cluster Head • A Cluster Head (CH) is a node that has been elected to take charge of a group of SNs. • A CH receives direct input from each of its SNs. • A CH forwards the data to base station or destination node through other CHs. • CHs use more energy than SNs.

7. Abnormal Node Behavior • Malicious Node • A node may be captured by the enemy at any point and start passing erroneous information or drop packets. • A node is more likely to become malicious if it has low energy or if it is surrounded by malicious nodes. • Selfish Node • A node may become selfish if its energy becomes low relative to its neighbors’. • “Selfish” can be thought of as “efficient”. If a node recognizes that its battery level is low and its neighbors have sufficient energy, it may start dropping packets so its neighbors pick up more of the burden. • The challenge becomes: How do we create a model such that malicious and selfish nodes can be identified and the WSN can adjust to these conditions to achieve a near-optimal performance?

8. System Model • Leveraging a two-level hierarchy in the WSN, the protocol is conducted using periodic peer-to-peer trust evaluation between two SNs and two CHs. • Each SN reports it p2p evaluation result to other SNs in the cluster and its CH. • The CHs perform CH-to-SN trust evaluation towards SNs in its cluster. • Each CH reports it p2p evaluation result to other CHs in the system to other CHs and the base station.

9. How Does Trust Factor In? • Once the hierarchy is established, the evaluations completed by each node follow a trust scheme that allows for direct and indirect trust-based reporting. • Trust Composition includes both social trust and QoS trust. • Social trust: intimacy, honesty, privacy, centrality and connectivity. • QoS trust: competence, cooperativeness, reliability, task completion capability. • In this work we consider intimacy, honesty, energy, unselfishness

10. Trust metrics • Intimacy • Reflects the relative degree of interaction experiences between two nodes • The more positive experiences SN A had with SN B, the more trust and confidence SN A will have toward SN B • Honesty • Implies whether a node is malicious or not • Energy • Measures if a SN is competent in performing its intended function • Unselfishness • Reflects if a SN can cooperatively execute the intended protocol.

11. Hierarchical Trust Management Protocol • Peer-to-peer trust evaluation • SN-levels • CH-levels • CH-to-SN Trust Evaluation • Station-to-CH Trust Evaluation

12. Evaluation Process • A weighted evaluation is performed and all four metrics are factored into one, overall trust score: • Tij(t) denotes the trust that node i has toward node j at time t. • Deciding the best values of w1, w2, w3, and w4 to maximize application performance is a trust formation issue which is explored in this paper.

13. Peer-to-Peer Trust Evaluation • P2P Trust Evaluation is performed between SNs and between CHs. • When node i evaluates its trust toward a neighbor node j • It snoops, or overhears enough data to provide direct observation. • i should also refer to past experiences. • When i evaluates a node that is beyond its communication range • it will use its past experiences. • It must also use recommendations from its 1-hop neighbors.

14. Peer-to-Peer Trust Evaluation • This relationship is represented as follows: • γ and α represent weights associated with trust decay. X represents one of the four trust components.

15. Peer-to-Peer Trust Factors • This measures the level of interaction experiences. It is computed by the number of interactions between node i and j over the maximum number of interactions between node i and any neighbor node over the time period [0, t]. • This refers to the belief of node ithat node j is honest based on node i’s direct observations toward node j. • It’s estimated by keeping a count of suspicious dishonest experiences of node j which node I has observed during [0, t] using a set of anomaly detection rules. • If the count exceeds a system-defined threshold, the value is 0. • Otherwise, the value is 1 minus the ratio of the count to the threshold.

16. Peer-to-Peer Trust Factors • This refers to the belief of node ithat node j still has adequate energy (representing competence) to perform its intended function. • It is measured by the percentage of node j’s remaining energy • It is estimated utilizing some energy consumption model • This provides the degree of unselfishness of node j as evaluated by node i based on direct observation over [0, t]. • Node i may apply overhearing and snooping techniques to detect selfish behaviors of node j.

17. Peer-to-Peer Trust Evaluation • This relationship is represented as follows: • When i evaluates a node that is not 1-hop neighbor • use its past experience • use recommendations from its 1-hop neighbors

18. Parameters Defined • α - Weight that represents a more instantaneous evaluation, since the higher α, the more weight is given to time t. • γ – weight between recommendations vs. past experiences • β – Represents the impact of “indirect recommendations”. • indirect recommendations is normalized to βTik(t) relative to 1 assigned to past experiences

19. CH-to-SN Trust Evaluation • Once all calculations are complete for a given time period t, the CH applies statistical analysis principles to all Tij(t) values received to perform CH-to-SN trust evaluation toward node j. • CH can also detect any outliers in the cluster to see if any good-mouthing or bad-mouthing is occurring. • The CH can exclude a sensor from reading and routing duties.

20. Station-to-CH Trust Evaluation • CH-to-CH trust evaluation is peer-to-peer. • Station-to-CH trust evaluation performs in a similar way as CH-to-SN evaluation.

21. Performance Model • A Stochastic Petri Net model is used to provide a basis for obtaining ground truth status of nodes in the system. • It derives objective trust against which subjective trust obtained as a result of executing our hierarchical trust management protocol can be checked and validated.

22. Petri Net Model - Energy • Place Energy indicates the remaining energy level of the node • A token will be released from place Energy when transition T_ENERGY is triggered. • The rate of transition T_ENERGY indicates the energy consumption rate. • Energy consumption rates: • Normal nodes • Selfish nodes

23. Petri Net Model - Selfishness • A node may become selfish to save energy. • An unselfish node may turn selfish in every trust evaluation interval Δt according to its remaining energy and the number of unselfish neighbors around. • A selfish node may redeem itself as unselfish to achieve a service availability goal. • Putting a token into place SN when transition T_SELFISH is triggered and removing the token from place SN when transition T_REDEMP is triggered

24. Petri Net Model - Compromise • A node becomes compromised when T_COMPRO fires and places a token in CN. • Model the IDS behavior through transition T_IDS • Rate is for compromised nodes • for good nodes (typo error)

25. Subjective Trust Evaluation • If j is a selfish node (a/c), compromised node (b/c) or normal node (c/c) • a, b and c: The average numbers of interactions of node i with a selfish node, a compromised node and a normal node

26. Objective Trust Evaluation • Compute objective trust based on actual status as provided by the SPN model output using exactly the same status value assignment as shown in Table I to yield ground truth status of node j at time t. • Tj,obj(t), is also a weighted linear combination of four trust component values

27. Trust Evaluation Results

28. Trust Evaluation Results • The trust evaluation consists of two parts • trust composition and trust aggregation • trust formation • Assertion • each trust property X has its own best α and β values • subjective assessment would be the most accurate against actual status of node j in trust property X • because different trust properties have their own intrinsic trust nature and react differently to trust decay over time

29. Trust Evaluation Results • Larger α indicates that subjective trust evaluation relies more on direct observations compared with past experiences • Larger β indicates that subjective trust evaluation relies more on indirect recommendations provided by recommenders compared with past experiences

30. Trust Evaluation Results • The best α and β values intrinsically depend on the nature of each trust property as well as a given set of parameter values • Subjective trust obtained as a result of executing our proposed hierarchical trust management protocol approaches true objective trust

31. Trust-based Geographic Routing • Geographic routing • a node disseminates a message to a maximum of L neighbors closest to the destination node • Trust-based geographic routing • node i forwards a message to a maximum of L neighbors not only closest to the destination node but also with the highest trust values Tij(t) • Baseline routing protocols • flooding-based • a node floods a message to all its neighbors • traditional geographic routing

32. Best Trust Formation to Maximize Application Performance • Identify weights to assign to individual trust properties • w1=w2=0.5 × wsocial • w3=w4=0.5 × wQoS • wsocial+ wQoS = 1 • Considering both social and QoS trust properties helps generate a higher message delivery ratio

33. Dynamic Trust Management • Dynamically adjust wsocial (the X coordinate) to optimize application performance in message delivery ratio

34. Performance Comparison • Outperforms traditional geographic routing • Approaches flooding-based routing

35. Performance Comparison • Traditional geographic routing performs better than trust-based geographic routing in message delay • This is expected

36. Performance Comparison • Incurs more message overhead than traditional geographic routing • the path selected by trust-based geographic routing is often the most trustworthy path, not necessarily the shortest path

37. Trust-based Intrusion Detection • Describe the algorithm that can be used by a high-level node such as a CH (or a base station) to perform trust-based intrusion detection of the SNs • Develop a statistical method to assess trust-based IDS false positive and false negative probabilities

38. Algorithm for Trust-Based Intrusion Detection • Selecting a system minimum trust threshold, Tth, below which a node is considered compromised • A compromised node will exhibit several social and QoStrust behaviors

39. Best Trust Formation to Maximize Application Performance • As the minimum trust threshold Tth increases, the false negative probability Pfn decreases while the false positive probability Pfp increases. • There exists an optimal trust threshold Tth,opt at which both false negative and false positive probabilities are minimized.

40. Performance Comparison • Presented are the best results of all three IDS schemes

41. Conclusion • Proposed a hierarchical dynamic trust management protocol for cluster-based wireless sensor networks, considering two aspects of trustworthiness, namely, social trust and QoS trust. • Developed a probability model utilizing stochastic Petri nets techniques to analyze the protocol performance, and validated subjective trust against objective trust obtained based on ground truth node status • Demonstrated the feasibility of dynamic hierarchical trust management and application-level trust optimization design concepts with trust-based geographic routing and trust-based IDS applications