Research Article
Subjective logic based trust model for mobile ad hoc networks
@INPROCEEDINGS{10.1145/1460877.1460916, author={Venkat Balakrishnan and Vijay Varadharajan and Uday Tupakula}, title={Subjective logic based trust model for mobile ad hoc networks}, proceedings={4th International ICST Conference on Security and Privacy in Communication Networks}, publisher={ACM}, proceedings_a={SECURECOMM}, year={2008}, month={9}, keywords={Trust Reputation Subjective logic Security and MANET}, doi={10.1145/1460877.1460916} }
- Venkat Balakrishnan
Vijay Varadharajan
Uday Tupakula
Year: 2008
Subjective logic based trust model for mobile ad hoc networks
SECURECOMM
ACM
DOI: 10.1145/1460877.1460916
Abstract
In last five years, several trust models have been proposed to enhance the security of Mobile Ad hoc Networks (MANET). Nevertheless, these trust models fail to express the notion of ignorance during the establishment of trust relationships between mobile nodes. Furthermore, they lack a well-defined approach to defend against the issues resulting from recommendations. In this paper, we propose a novel subjective logic based trust model that enables mobile nodes to explicitly represent and manage ignorance as uncertainty during the establishment of trust relationships with other nodes. Our model defines additional operators to subjective logic in order to address the ignorance introduced between mobile nodes (which have already established trust relationships) as a result of mobility-induced separation. Second, we demonstrate on how mobile nodes formulate their opinions for other nodes based on the evidence collected from the benign and malicious behaviors of those nodes. We then describe on how mobile nodes establish trust relationships with other nodes using the opinions held for those nodes. Depending on the policies defined, these relationships are then used by our model to enhance the security of mobile communications. Third, we propose a novel approach to communicate recommendations by which no explicit packets or additional headers are disseminated as recommendations. This allows our model to defend against recommendation related issues such as free-riding, honest-elicitation, and recommender's bias. Finally, we demonstrate the performance of our model through NS2 simulations.