ACO-BTM: A Behavior Trust Model in Cloud Computing Environment

Abstract

Considering trust issues in cloud computing, we analyze the feasibility of adopting ant colony optimization algorithm to simulate trust relationships between entities in the cloud and then propose a novel behavior trust model: ACO-BTM. Trust relationships between entities in cloud computing are dynamic, uncertain and hard to quantify. ACO-BTM introduces the conception of ‘pheromone’ and transition probability to represent behavior trust. Then, it focuses on the research of dynamic trust evaluation, time constraint and some other issues. Furthermore, a detailed algorithm process of behavior trust evaluation is given in this context. Finally, ACO-BTM is applied to cloud computing platform to simulate the establishment of behavior trust relationships. The simulation experiment verifies that trust degree change with time varies and the frequency of interactions. Compared with the other model, ACO-BTM can provide better trust recommendation services and protect against attacks of malicious nodes effectively in cloud computing environment. It is proved that ACO-BTM has good flexibility, accuracy and robustness.

Keywords

• cloud computing
• behavior trust
• ant colony optimization
• trust pheromone
• heuristic pheromone