用相似模型实时评估协作者可信度，提升任务协同效率。
Rapid and Continuous Trust Evaluation for Effective Task Collaboration Through Siamese Model
- 构建带属性的控制流图，表征协作者状态
- 仅需少量数据快速收敛，异常检测率高
- 适合动态环境下的实时可信度评估
信任正成为保障协作系统中任务成功完成的有效工具。然而，由于设备分布、环境复杂及资源动态变化，任务执行过程中快速持续地评估协作者的可信度仍具挑战。为此，本文提出一种基于孪生网络的快速连续信任评估框架（SRCTE），以促进有效任务协同。首先，收集可信状态下协作者的通信与计算资源属性及历史协作数据，利用带属性的控制流图（ACFG）表示，捕捉与信任相关的语义信息，并作为比较基准。在任务执行的每个时间片内，实时采集协作者的资源属性与任务完成效果，同样以ACFG形式表示其信任相关语义。采用由两个共享参数的Structure2vec网络组成的孪生模型，学习每对ACFG的深层语义并生成嵌入向量。最后，通过计算嵌入向量间的相似度，确定每个时间片下协作者的信任值。使用两台Dell EMC 5200服务器和一台Google Pixel 8搭建真实系统进行测试。实验结果表明，SRCTE仅需少量数据即可快速收敛，相比基线算法具有更高的异常信任检测率。
原文摘要 · Abstract (English)
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.
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