2026-10-14T14:43:11Z https://www.scidb.cn/oai

10.57760/sciencedb.j00173.00069 2026-06-18T16:28:13Z

2026-06-18 260115 Enhance publishing|One-step Reconstruction Diffusion Model-based Poisoning Attack onQoS-aware Cloud API Recommender Systems doi:10.57760/sciencedb.j00173.00069 en Solemnly declare: If you use this open source content in papers, books, academic reports and other works, please quote the following documents (the original link has the latest citation format):Tan Zeyu, Wang Haoyuan, Qi Mingyang, Sun Mengmeng, Shen Limin, Chen Zhen. A one-step reconstruction diffusion model poisoning attack for QoS aware cloud API recommendation system [J]. Journal of Electronics and Information Technology doi: 10.11999/JEIT260115Authors: TAN Zeyu, WANG Haoyuan, QI Mingyang, SUN Mengmeng, SHEN Limin, CHEN Zhen.Author： School of Artificial Intelligence, Yanshan University, Key Laboratory of Computer Virtual Technology and System Integration, Hebei Province DOI： 10.11999/JEIT260115 Original: https://jeit.ac.cn/article/doi/10.11999/JEIT260115 Correspondents: Chen Zhen, zhenchen@ysu.edu.cn Open source date: June 18, 2026 Fund projects: National Natural Science Foundation of China (No.62102348), the Hebei Natural Science Foundation(No.F2022203012), the Science and Technology Program of Hebei (No.236Z0103G), the Innovation Capability Improvement Plan Project of Hebei Province (No.22567626H) Graduate Innovation Fund of Hebei Province (No. CXZZS2025039) Open source content One step reconstruction of diffusion model for QoS aware cloud API recommendation system, poisoning attack - reproducing code Abstract: The Quality of Service (QoS) aware cloud application programming interface (API) recommendation system effectively alleviates the information overload challenge caused by the continuous growth of cloud APIs by guiding users to discover high-quality cloud APIs. However, existing research on QoS aware cloud API recommendation systems mainly focuses on improving recommendation accuracy, ignoring the security risks brought by poisoning attacks. Therefore, this study proposes a preference guided poisoning attack framework (PDPA) based on a one-step reconstruction diffusion model from the perspective of attack learning defense to simulate poisoning attacks and reveal the vulnerability of cloud API recommendation systems. Firstly, PDPA uses a one-step reconstruction diffusion model to separately model the QoS and call distribution of real users regarding cloud APIs, generating false user QoS and call behavior similar to real users. Next, PDPA selects fake users who have a preference for calling the target cloud API to simulate poisoning attacks, effectively reducing the interference of the target cloud API on the concealment of fake users and ensuring the effectiveness of fake user attacks. Finally, extensive experiments were conducted on real-world datasets, and the results demonstrated the vulnerability of QoS aware cloud API recommendation systems under poisoning attacks, as well as the superior attack effectiveness and concealment of false users generated by PDPA compared to baseline methods.  Recommender system; Poisoning attack; Quality of Service(QoS); Diffusion model; Preferenceguidance tan ze yu RESTRICTED dataset http://www.doi.org/10.11999/JEIT260115 Science Data Bank