A security detection approach based on autonomy-oriented user sensor in social recommendation network
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 relations...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501329221082415 |
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| _version_ | 1832559681358266368 |
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| author | Shanshan Wan Ying Liu |
| author_facet | Shanshan Wan Ying Liu |
| author_sort | Shanshan Wan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2f8924e6f37d4c86a4cc625292100189 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2022-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-2f8924e6f37d4c86a4cc6252921001892025-02-03T01:29:28ZengWileyInternational Journal of Distributed Sensor Networks1550-14772022-03-011810.1177/15501329221082415A security detection approach based on autonomy-oriented user sensor in social recommendation networkShanshan WanYing LiuUser 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.https://doi.org/10.1177/15501329221082415 |
| spellingShingle | Shanshan Wan Ying Liu A security detection approach based on autonomy-oriented user sensor in social recommendation network International Journal of Distributed Sensor Networks |
| title | A security detection approach based on autonomy-oriented user sensor in social recommendation network |
| title_full | A security detection approach based on autonomy-oriented user sensor in social recommendation network |
| title_fullStr | A security detection approach based on autonomy-oriented user sensor in social recommendation network |
| title_full_unstemmed | A security detection approach based on autonomy-oriented user sensor in social recommendation network |
| title_short | A security detection approach based on autonomy-oriented user sensor in social recommendation network |
| title_sort | security detection approach based on autonomy oriented user sensor in social recommendation network |
| url | https://doi.org/10.1177/15501329221082415 |
| work_keys_str_mv | AT shanshanwan asecuritydetectionapproachbasedonautonomyorientedusersensorinsocialrecommendationnetwork AT yingliu asecuritydetectionapproachbasedonautonomyorientedusersensorinsocialrecommendationnetwork AT shanshanwan securitydetectionapproachbasedonautonomyorientedusersensorinsocialrecommendationnetwork AT yingliu securitydetectionapproachbasedonautonomyorientedusersensorinsocialrecommendationnetwork |