基于推荐的抗攻击电子商务信任模型

针对目前在线信誉系统面临的自我提升攻击、恶意诋毁攻击和漂白攻击三类常见的攻击，提出了一种基于推荐的集中式信任模型。该模型综合了用户间购买行为的相似度以及评价的时效性确定对某件商品的推荐信任值，同时结合基于自身交易经验的直接信任值，来获取对该商品的综合信任度。通过在真实交易数据中注入三类攻击进行仿真，实验结果证明，相对于现有的信誉管理模型，该模型能更好地抵御攻击，为消费者提供更准确的卖家和商品信誉值。

Recommendation-based anti-attack trust model on E-commerce

A recommendation-based centralized trust model was proposed to deal with three kinds of common attacks including self-promoting attack, slandering attack and whitewashing attack. The model calculated the similarity of consumption custom between buyers and the timeliness of the buyers' evaluation opinions to determine the recommendation trust value, and obtained the direct trust value on the basis of the buyer's own trading experience. According to the direct and recommendation trust value, composite trust value could be calculated. Simulations were given by injecting the three kinds of attacks into real transaction data. The results prove that, compared with the existing reputation management models, the proposed model can effectively prevent attacks, and provides more accurate reputation information of sellers and goods to consumers.