安全推荐系统中基于信任的检测模型

A Trust-Based Detecting Mechanism in Secure Recommender Systems

在网格环境中,推荐系统通过提供高品质的个性化推荐,帮助网格用户选择更好的服务。另外,推荐系统也应用于虚拟机管理平台来评估虚拟机的性能和可靠性。然而,推荐结果对用户偏好信息的敏感性使得推荐系统易受到人为攻击(用户概貌注入攻击或托攻击)。本文中,我们提出并评估了一种新的基于信任的安全检测算法以保护推荐系统抵御用户概貌注入攻击。并且,我们分别在用户级和项目级上讨论了信任检测与RDMA检测的结合。最后,我们通过试验表明这些新的安全检测机制可以取得更好的检测精度。

Recommender systems could be applied in grid environment to help grid users select more suitable services by making high quality personalized recommendations. Also,recommendation could be employed in the virtual machines managing platform to measure the performance and creditability of each virtual machine. However,such systems have been shown to be vulnerable to profile injection attacks （shilling attacks）,attacks that involve the insertion of malicious profiles into the ratings database for the purpose of altering the system＇s recommendation behavior. In this paper we introduce and evaluate a new trust-based detecting algorithm for protecting recommender systems against profile injection attacks. Moreover,we discuss the combination of our trus-based metrics with previous metrics such as RDMA in profile-level and item-level respectively. In the end,we show these metrics can lead to improved detecting accuracy experimentally.