Data poisoning attack detection approach for quality of service aware cloud API recommender system
To solve the problem that existing studies usually assumed that the QoS data of cloud API recommender system was reliable, ignoring the data poisoning attack on cloud API recommender system by malicious users in open network environment, a data poisoning attack detection approach based on multi-feat...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2023-08-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023161/ |
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| _version_ | 1850121580923846656 |
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| author | Zhen CHEN Wenchao QI Taiyu BAO Limin SHEN |
| author_facet | Zhen CHEN Wenchao QI Taiyu BAO Limin SHEN |
| author_sort | Zhen CHEN |
| collection | DOAJ |
| description | To solve the problem that existing studies usually assumed that the QoS data of cloud API recommender system was reliable, ignoring the data poisoning attack on cloud API recommender system by malicious users in open network environment, a data poisoning attack detection approach based on multi-feature fusion was proposed.Firstly, a user connected network graph was constructed based on the designed similarity function, and users’ neighborhood features were captured using Node2vec.Secondly, sparse auto-encoder was used to mine user QoS deep feature, and user interpretation feature based on QoS data weighted average deviation was designed.Furthermore, a fake user detection model based on support vector machine was established by integrating user neighborhood feature, QoS deep feature, and interpretation feature, the model parameters were learned using grid search and alternating iterative optimization strategy to complete fake user detection.Finally, the effectiveness and superiority of the proposed approach were verified through extensive experiments, realizing the poison attack defense against QoS aware cloud API recommender system at the data side. |
| format | Article |
| id | doaj-art-90deefb4dbb14793bd39b893818d1bf6 |
| institution | OA Journals |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-08-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-90deefb4dbb14793bd39b893818d1bf62025-08-20T02:35:04ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-08-014415516759385966Data poisoning attack detection approach for quality of service aware cloud API recommender systemZhen CHENWenchao QITaiyu BAOLimin SHENTo solve the problem that existing studies usually assumed that the QoS data of cloud API recommender system was reliable, ignoring the data poisoning attack on cloud API recommender system by malicious users in open network environment, a data poisoning attack detection approach based on multi-feature fusion was proposed.Firstly, a user connected network graph was constructed based on the designed similarity function, and users’ neighborhood features were captured using Node2vec.Secondly, sparse auto-encoder was used to mine user QoS deep feature, and user interpretation feature based on QoS data weighted average deviation was designed.Furthermore, a fake user detection model based on support vector machine was established by integrating user neighborhood feature, QoS deep feature, and interpretation feature, the model parameters were learned using grid search and alternating iterative optimization strategy to complete fake user detection.Finally, the effectiveness and superiority of the proposed approach were verified through extensive experiments, realizing the poison attack defense against QoS aware cloud API recommender system at the data side.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023161/recommender systemcloud APIquality of servicedata poisoningattack detection |
| spellingShingle | Zhen CHEN Wenchao QI Taiyu BAO Limin SHEN Data poisoning attack detection approach for quality of service aware cloud API recommender system Tongxin xuebao recommender system cloud API quality of service data poisoning attack detection |
| title | Data poisoning attack detection approach for quality of service aware cloud API recommender system |
| title_full | Data poisoning attack detection approach for quality of service aware cloud API recommender system |
| title_fullStr | Data poisoning attack detection approach for quality of service aware cloud API recommender system |
| title_full_unstemmed | Data poisoning attack detection approach for quality of service aware cloud API recommender system |
| title_short | Data poisoning attack detection approach for quality of service aware cloud API recommender system |
| title_sort | data poisoning attack detection approach for quality of service aware cloud api recommender system |
| topic | recommender system cloud API quality of service data poisoning attack detection |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023161/ |
| work_keys_str_mv | AT zhenchen datapoisoningattackdetectionapproachforqualityofserviceawarecloudapirecommendersystem AT wenchaoqi datapoisoningattackdetectionapproachforqualityofserviceawarecloudapirecommendersystem AT taiyubao datapoisoningattackdetectionapproachforqualityofserviceawarecloudapirecommendersystem AT liminshen datapoisoningattackdetectionapproachforqualityofserviceawarecloudapirecommendersystem |