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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.<\/jats:p>","DOI":"10.1177\/15501329221082415","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T07:13:02Z","timestamp":1648537982000},"page":"155013292210824","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["A security detection approach based on autonomy-oriented user sensor in social recommendation network"],"prefix":"10.1177","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3421-4387","authenticated-orcid":false,"given":"Shanshan","family":"Wan","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, P.R. China"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, P.R. China"}]}],"member":"179","published-online":{"date-parts":[[2022,3,29]]},"reference":[{"key":"bibr1-15501329221082415","volume-title":"Big Data: principles and best practices of scalable realtime data systems","author":"Warren J","year":"2015"},{"key":"bibr2-15501329221082415","volume-title":"2015 international conference on behavioral, economic and socio-cultural computing (BESC)","author":"Xiong H"},{"key":"bibr3-15501329221082415","volume-title":"Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining","author":"Wang X"},{"key":"bibr4-15501329221082415","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2020.3030387"},{"key":"bibr5-15501329221082415","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1485\/1\/012017"},{"key":"bibr6-15501329221082415","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-017-2720-6"},{"key":"bibr7-15501329221082415","volume-title":"Proceedings of the 22nd ACM international conference on multimedia","author":"Wang X"},{"key":"bibr8-15501329221082415","doi-asserted-by":"crossref","unstructured":"Golbeck J. 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