A unified trust model for pervasive computing environment
Pervasive systems are weaving themselves in our daily life by making it possible for known and even unknown parties to collect user information invisibly and in an unobtrusive manner. The huge number of interactions between users and pervasive devices necessitate a comprehensive trust model which un...
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| 格式: | Thesis |
| 語言: | English |
| 出版: |
2013
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| 主題: | |
| 在線閱讀: | http://eprints.utm.my/id/eprint/36697/5/HamedKhiabaniPFSKSM2013.pdf |
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| 總結: | Pervasive systems are weaving themselves in our daily life by making it possible for known and even unknown parties to collect user information invisibly and in an unobtrusive manner. The huge number of interactions between users and pervasive devices necessitate a comprehensive trust model which unifies different trust factors such as context, recommendation, and history that would be used to calculate precisely the trust level of each party. Therefore, developing a runtime and accurate trust computation would be a major issue in these environments. Measuring accurately the integrity of nodes willing to interact with each other can enhance the trust calculation process, particularly during the uncertainty state and initiation phase. Trusted computing enables effective solutions to verify the trustworthiness of computing platforms. This research aims to provide a unified and dynamic approach while considering several trust dimensions namely: history, recommendation, context, and attesting the communicating platforms to increase accuracy of trust computation mechanism. In this research, the Unified Trust Model (UTM) is proposed to calculate trustworthiness of entities based on history, recommendation, context, and platform integrity measurement (used in remote attestation). The accuracy and performance of UTM were evaluated using a simulation-based method in different experimental scenarios. A comparison of UTM with similar works showed that the accuracy of the model improved from 2% to 41.3% during an oscillating attack and from 7.4% to 26.8% during a collusion attack. The results obtained from the different simulated scenarios have demonstrated that the proposed UTM is highly accurate and can be used effectively in realistic as well as low interaction environments. |
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