Trust Management in Mobile Ad Hoc Networks Using a

Transcript:Trust Management in Mobile Ad Hoc Networks Using a:
Trust Management in Mobile Ad Hoc Networks Using a Scalable Maturity-Based Model Authors: Pedro B. Velloso, Rafael P. Laufer, Daniel de O. Cunha, Otto Carlos M. B. Duarte, and Guy Pujolle Paper Presentation By : Gaurav Dixit (gdixit@vt.edu) Outline Introduction Trust Model Implementation Results MANets - same node can work as router server client Assumption of good behavior – Not true! Trust needs to be measured - This paper provides one such method. Applying human trust dynamics to trust calculation of nodes Builds on recommendations Introduction Trust level of a node depends on:= (previous individual experiences) + (recommendation from neighbors) Benefits of trust calculation: avoid sending packets to malicious nodes. increased co-operation among good nodes. Recommendations collected only from neighbors. Advantages for nodes: Less storage Less power requirement Less processing Better for changing topologies – information for entire network not required Since, recommendations not forwarded, it is good for networks: Less recommendation messages travelling in network - low traffic Low energy consumption for entire network Relationship Maturity Similar to human trust behavior, more weightage is given to the recommendations from older neighbors. Trust Model Trust level assigned to each neighbor. Trust value reflects behavior history, and thus expected future behavior. Node forms opinion based on experiences. Transmission of these opinions about node i are called recommendations. Trust Model … Recommendations compensate for lack of monitoring capabilities. Paper defines Recommendation Exchange Protocol (REP) Trust Model… Trust level varies from 0 to 1. Recommendation from C more important than that from B, because of relationship maturity. Trust Model: Architecture Two parts: Learning Plan: gathers and converts information into knowledge. Trust plan: assess trust level of each neighbor using stored knowledge and recommendations. Trust Model: Components Trust Model: Components Behavior monitor observes network, indicates new neighbors to Rec Manager, and send behavior report to Classifier. Classifier sends behavior classification to Experience Calculator. Trust Calculator calculates trust with inputs from experiences and recommendations. Auxiliary Trust Table entries correspond to relationship maturity. Trust table entries have timeout. Trust Model: Components Three operation modes: Simple: Just trust table, REP optional Intermediate: Simple mode plus storage of recommendations Advanced: Complete system implementation. Recommendation Manager implements REP. All nodes are in advanced mode in this paper. Trust level evaluation 𝑇𝑎(𝑏) = (1 − 𝛼)𝑄𝑎(𝑏) + 𝛼𝑅𝑎(𝑏) 𝑄𝑎(𝑏) = 𝛽𝐸𝑎(𝑏) + (1 − 𝛽)𝑇𝑎(𝑏) Ta(b) ->Trust calculation from node a for node b Qa(b) -> Personal Experience Ra(b) ->