4. Conclusions

Trust evaluation model is of importance to supporting system security. This paper has presented a trust evaluation model based on evidence theory and sliding windows for cloud computing. The proposed model has a number of advantages as follows. Firstly, it is simple to be executed. The time complexity of our algorithm is O(n×m) if there are n CSPs and m CUs in the system. Secondly, the timeliness of interactions is reflected by introducing sliding windows. In sliding window mechanism, interactions are divided into valid interactions and invalid interactions. Only valid interactions can affect the trust degree of entities. So it improved the extensibility of the system. Thirdly, the trust degree of entities changes dynamically according to the behavior of entities based on D-S evidence theory. We evaluate the trust of both the CSPs and the CUs. In this way, we can provide security protection for the CSPs and the CUs. Finally, it can help the system identifying malicious entities to some extent and improve the success interaction rate. It enhances the anti-attack of the system. Simulation experiments show that the trust degree of entities increases slowly and decreases quickly using our model. It can effectively identify malicious entities, and provide reliable information to correctly make the security decisions for the system. Future, we will look for ways to overcome the collusive deception behaviors. And the data mining and knowledge discovery method will be combined with our trust evaluation model to evaluate the changes of CUs and CSPs.