Page 370 - 《软件学报》2026年第6期
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陈真 等: 可信数据增强的 QoS 感知云 API 推荐系统投毒攻击持续防御 2689
[18] Shao LS, Zhang J, Wei Y, Zhao JF, Xie B, Mei H. Personalized QoS prediction for Web services via collaborative filtering. In: Proc. of
the 2007 IEEE Int’l Conf. on Web Services. Salt Lake City: IEEE, 2007. 439–446. [doi: 10.1109/ICWS.2007.140]
[19] Zheng ZB, Ma H, Lyu MR, King I. WSRec: A collaborative filtering based Web service recommender system. In: Proc. of the 2009 IEEE
Int’l Conf. on Web Services. Los Angeles: IEEE, 2009. 437–444. [doi: 10.1109/ICWS.2009.30]
[20] Chen YP, Zhang YQ, Xia H, Gao C, Wang ZM, Wang FW, Li G. A hybrid tensor factorization approach for QoS prediction in time-
aware mobile edge computing. Applied Intelligence, 2022, 52(7): 8056–8072. [doi: 10.1007/s10489-021-02851-z]
[21] Nan ZH, Zhao F. Research on semi-supervised recommendation algorithm based on hybrid model. In: Proc. of the 2nd Int’l Conf. on
Machine Learning, Big Data and Business Intelligence. Taiyuan: IEEE, 2020. 344–348. [doi: 10.1109/MLBDBI51377.2020.00073]
[22] Sun D, Nie T. A Web service recommendation algorithm based on BaisSVD. In: Proc. of the 5th IEEE Information Technology and
Mechatronics Engineering Conf. Chongqing: IEEE, 2020. 29–32. [doi: 10.1109/ITOEC49072.2020.9141664]
[23] Anwar T, Uma V, Srivastava G. Rec-CFSVD++: Implementing recommendation system using collaborative filtering and singular value
decomposition (SVD)++. Int’l Journal of Information Technology & Decision Making, 2021, 20(4): 1075–1093. [doi: 10.1142/S02196
22021500310]
[24] Yang YT, Zheng ZB, Niu XD, Tang MD, Lu YT, Liao XK. A location-based factorization machine model for Web service QoS
prediction. IEEE Trans. on Services Computing, 2021, 14(5): 1264–1277. [doi: 10.1109/TSC.2018.2876532]
[25] Zhang PY, Huang WJ, Chen YT, Zhou MC, Al-Turki Y. A novel deep-learning-based QoS prediction model for service recommendation
utilizing multi-stage multi-scale feature fusion with individual evaluations. IEEE Trans. on Automation Science and Engineering, 2024,
21(2): 1740–1753. [doi: 10.1109/TASE.2023.3244184]
[26] OWASP. OWASP API Security Top Ten—2023. 2023. https://owasp.org/API-Security/editions/2023/en/0x00-header/.
[27] Gunes I, Kaleli C, Bilge A, Polat H. Shilling attacks against recommender systems: A comprehensive survey. Artificial Intelligence
Review, 2014, 42(4): 767–799. [doi: 10.1007/s10462-012-9364-9]
[28] Chirita PA, Nejdl W, Zamfir C. Preventing shilling attacks in online recommender systems. In: Proc. of the 7th Annual ACM Int’l
Workshop on Web Information and Data Management. Bremen: ACM, 2005. 67–74. [doi: 10.1145/1097047.1097061]
[29] Zhou QQ, Wu JX, Duan LL. Recommendation attack detection based on deep learning. Journal of Information Security and Applications,
2020, 52: 102493. [doi: 10.1016/j.jisa.2020.102493]
[30] Mehta B. Unsupervised shilling detection for collaborative filtering. In: Proc. of the 22nd National Conf. on Artificial Intelligence.
Vancouver: AAAI Press, 2007. 1402–1407.
[31] Zhang F, Deng ZJ, He ZM, Lin XC, Sun LL. Detection of shilling attack in collaborative filtering recommender system by PCA and data
complexity. In: Proc. of the 2018 Int’l Conf. on Machine Learning and Cybernetics. Chengdu: IEEE, 2018. 673–678. [doi: 10.1109/
ICMLC.2018.8526965]
[32] Wu ZA, Cao J, Mao B, Wang YQ. Semi-SAD: Applying semi-supervised learning to shilling attack detection. In: Proc. of the 5th ACM
Conf. on Recommender Systems. Chicago: ACM, 2011. 289–292. [doi: 10.1145/2043932.2043985]
[33] Chen J, Zhang XX, Zhang R, Wang C, Liu L. De-Pois: An attack-agnostic defense against data poisoning attacks. IEEE Trans. on
Information Forensics and Security, 2021, 16: 3412–3425. [doi: 10.1109/TIFS.2021.3080522]
[34] Chen Z, Qi WC, Bao TY, Shen LM. Data poisoning attack detection approach for quality of service aware cloud API recommender
system. Journal on Communications, 2023, 44(8): 155–167 (in Chinese with English abstract). [doi: 10.11959/j.issn.1000-436x.2023161]
[35] Toker A, Eisenberger M, Cremers D, Leal-Taixé L. SatSynth: Augmenting image-mask pairs through diffusion models for aerial semantic
segmentation. In: Proc. of the 2024 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Seattle: IEEE, 2024. 27685–27695.
[doi: 10.1109/CVPR52733.2024.02615]
[36] Cao HQ, Tan C, Gao ZY, Xu YL, Chen GY, Heng PA, Li SZ. A survey on generative diffusion models. IEEE Trans. on Knowledge and
Data Engineering, 2024, 36(7): 2814–2830. [doi: 10.1109/TKDE.2024.3361474]
[37] Wang WJ, Xu YY, Feng FL, Lin XY, He XN, Chua TS. Diffusion recommender model. In: Proc. of the 46th Int’l ACM SIGIR Conf. on
Research and Development in Information Retrieval. Taipei: ACM, 2023. 832–841. [doi: 10.1145/3539618.3591663]
[38] Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. In: Proc. of the 34th Int’l Conf. on Neural Information Processing
Systems. Vancouver: NeurIPS, 2020. 6840–6851.
附中文参考文献
[8] 张以文, 项涛, 郭星, 贾兆红, 何强. 基于 SOM 神经网络的服务质量预测. 软件学报, 2018, 29(11): 3388–3399. http://www.jos.org.cn/

