Rapid and Continuous Trust Evaluation for Effective Task Collaboration Through Siamese Model

Trust is emerging as an effective tool to ensure the successful completion of collaborative tasks within collaborative systems. However, rapidly and continuously evaluating the trustworthiness of collaborators during task execution is a significant challenge due to distributed devices, complex operational environments, and dynamically changing resources. To tackle this challenge, this paper proposes a Siamese-enabled rapid and continuous trust evaluation framework (SRCTE) to facilitate effective task collaboration. First, the communication and computing resource attributes of the collaborator in a trusted state, along with historical collaboration data, are collected and represented using an attributed control flow graph (ACFG) that captures trust-related semantic information and serves as a reference for comparison with data collected during task execution. At each time slot of task execution, the collaborator's communication and computing resource attributes, as well as task completion effectiveness, are collected in real time and represented with an ACFG to convey their trust-related semantic information. A Siamese model, consisting of two shared-parameter Structure2vec networks, is then employed to learn the deep semantics of each pair of ACFGs and generate their embeddings. Finally, the similarity between the embeddings of each pair of ACFGs is calculated to determine the collaborator's trust value at each time slot. A real system is built using two Dell EMC 5200 servers and a Google Pixel 8 to test the effectiveness of the proposed SRCTE framework. Experimental results demonstrate that SRCTE converges rapidly with only a small amount of data and achieves a high anomaly trust detection rate compared to the baseline algorithm.