A security detection approach based on autonomy-oriented user sensor in social recommendation network
A security detection approach based on autonomy-oriented user sensor in social recommendation network
复制标题
社交推荐网络中基于自主型用户传感器的安全检测方法
DOI：
10.1177/15501329221082415
复制
发表时间：
2022-03
影响因子：
2.3
通讯作者：
Ying Liu
中科院分区：
文献类型：
--
作者：
Shanshan Wan;Ying Liu
基于用户社交网络的推荐系统在当前推荐领域取得了显著的成绩。然而，开放性的特点给推荐系统的安全带来了很大的隐患。先令攻击者可以通过搜索用户关系来改变推荐。大多数先令攻击检测方法依赖于明确的用户历史数据来定位先令攻击者。社交网络中用户的信息传播、社会反馈等重要特征没有得到人们的重视。提出了一种基于自主用户传感器（AOUSD）的安全检测方法来识别先令攻击者。具体而言，(1)将用户模拟为具有自主能力的社交传感器；(2)基于社交传感器的信息传播、信息反馈和信息消失机制，构建用户交互模型，并考虑变时间函数，形成用户动态知识图；(3)采用分层聚类方法生成初步可疑候选组，并在动态知识图上采用图社区检测聚类方法检测攻击者。然后，首先在NetLogo上进行了AOUSD仿真，并与基于Amazon数据的其他算法进行了比较。实验结果证明了该方法在检测先令攻击的效率和准确性方面的优势。
User social network-based recommender system has achieved significant performance in current recommendation fields. However, the characteristic of openness brings great hidden dangers to the security of recommender systems. Shilling attackers can change the recommendations by foraging user relationships. Most shilling attack detection approaches depend on the explicit user historical data to locate shilling attackers. Some important features such as information propagation and social feedback of users in social networks have not been noticed. We propose a security detection method based on autonomy-oriented user sensor (AOUSD) to identify shilling attackers. Specifically, (1) the user is simulated as a social sensor with autonomous capabilities, (2) the user interaction model is built based on information propagation, information feedback and information disappearance mechanisms of social sensors, and a user dynamic knowledge graph is formed by considering the variable time function, (3) hierarchical clustering method is used to generate preliminary suspicious candidate groups and graph community detection clustering method is applied on the dynamic knowledge graph to detect the attackers. Then, AOUSD is first simulated on NetLogo and it is compared with other algorithms based on the Amazon data. The results prove the advantages of AOUSD in the efficiency and accuracy on shilling attack detection.
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DOI：
10.1016/j.knosys.2016.02.008
发表时间：
2015-06
期刊：
Knowl. Based Syst.
影响因子：
--
作者：
Zhihai Yang;Lin Xu;Zhongmin Cai;Zongben Xu
通讯作者：
Zhihai Yang;Lin Xu;Zhongmin Cai;Zongben Xu
DOI：
10.1007/978-3-319-18032-8_14
发表时间：
2015-05
期刊：
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影响因子：
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作者：
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DOI：
10.1016/j.physa.2017.04.048
发表时间：
2017-10
影响因子：
3.3
作者：
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影响因子：
2.7
作者：
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影响因子：
5
作者：
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