文献类型：期刊文献

英文题名：A priori trust inference with context-aware stereotypical deep learning

作者：Zhou, Peng[1];Gu, Xiaojing[2];Zhang, Jie[3];Fei, Minrui[1,4]

机构：[1]Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200041, Peoples R China;[2]E China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[3]Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore;[4]Shanghai Univ, Shanghai Key Lab Power Stn Automat Technol, Shanghai 200041, Peoples R China

年份：2015

卷号：88

起止页码：97

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20153501214614);WOS:【SCI-EXPANDED(收录号:WOS:000362611600009)】；

基金：The work was partially supported by National Natural Science Foundation of China under Grant Nos. 61502293 and 61205017, the Key Project of Science and Technology Commission of Shanghai Municipality under Grant No. 14JC1402200, the ASTAR/I2R-SERC, Public Sector Research Funding (PSF) Singapore (M4070212.020) and the Fundamental Research Funds for the Central Universities of China.

语种：英文

外文关键词：Multi-agent systems; Stereotypical trust model; Deep learning

摘要：In multi-agent systems, stereotypical trust models are widely used to bootstrap a priori trust in case historical trust evidences are unavailable. These models can work well if and only if malicious agents share some common features (i.e., stereotypes) in their profiles and these features can be detected. However, this condition may not hold for all the adversarial scenarios. Smart attackers can show different trustworthiness to different agents and services (i.e., launching context-correlated attacks). In this paper, we propose CAST, a novel Context-Aware Stereotypical Trust deep learning framework. CAST coins a comprehensive set of seven context-aware stereotypes, each of which can capture a unique type of context-correlated attacks, as well as a deep learning architecture to keep the trust stereotyping robust (i.e., resist training errors). The basic idea is to construct a multi-layer perceptive structure to learn the latent correlations between context-aware stereotypes and the trustworthiness, and thus can estimate the new trust by taking into account the context information. We have evaluated CAST using a rich set of experiments over a simulated multi-agent system. The experimental results have successfully confirmed that, our CAST can achieve approximately tens of times higher trust inference accuracy in average than the competing algorithms in the presence of context-correlated attacks, and more importantly can maintain a much better trust inference robustness against stereotyping errors. (C) 2015 Elsevier B.V. All rights reserved.