| 摘要: |
| 服务质量(quality of service, QoS)感知云API推荐系统在解决云API过载问题、差异化云API性能和实现高质量云API选择中具有重要作用. 但由于网络环境的开放性和云API的货币属性, 推荐系统易受到投毒攻击, 从而导致推荐结果偏离公平性和可信性. 现有防御方法主要采用“检测防御”策略, 即在模型训练前通过检测算法滤除恶意用户来缓解攻击影响, 但受限于检测算法性能, 不可避免地会出现无法将恶意用户全部滤除的情形. 为此, 从“以攻学防”的视角提出一种基于可信数据增强的QoS感知云API推荐系统投毒攻击持续防御方法. 首先构建基于可信数据增强的投毒攻击防御框架, 通过生成高质量可信用户数据并参与模型训练来增强推荐系统的鲁棒性. 其次, 设计基于扩散模型的可信用户生成算法. 采用迭代去噪的方式学习真实云API的QoS数据分布, 生成高质量的可信用户向量, 消解投毒攻击数据对训练模型的影响. 最后, 基于真实云API的QoS数据集进行大量实验, 利用3类11种推荐算法全面评估所提防御方法的有效性和普适性. 实验结果表明, 所提出的基于可信数据增强的投毒攻击持续防御框架是有效的, 生成的可信用户可显著提高云API推荐系统的鲁棒性. |
| 关键词: 推荐系统 云API 投毒攻击 持续防御 数据增强 扩散模型 |
| DOI：10.13328/j.cnki.jos.007565 |
| 分类号:TP393 |
| 基金项目:国家自然科学基金(62102348, 62276226); 中央引导地方科技发展资金(236Z7725G, 236Z0103G); 河北省自然科学基金(F2022203012); 河北省创新能力提升计划(22567626H) |
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| Continuous Defense Against Poisoning Attack with Trusted Data Augmentation for QoS-aware Cloud API Recommendation System |
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CHEN Zhen1,2, FAN Shuang1, YU Jian-Qiang1, XU Yue-Shen3, TAN Ze-Yu1, YOU Dian-Long1, SHEN Li-Min1
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1.School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China;2.Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province (Yanshan University), Qinhuangdao 066004, China;3.School of Computer Science and Technology, Xidian University, Xi’an 710071, China
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| Abstract: |
| Quality of service (QoS)-aware cloud API recommendation systems play an important role in solving cloud API overload problems, differentiating cloud API performance, and achieving high-quality cloud API selection. However, due to the openness of the network environment and the monetary nature of cloud APIs, recommendation systems are susceptible to poisoning attacks, which causes the recommendation results to deviate from fairness and credibility. Existing defense methods against poisoning attacks mainly adopt the “detection and defense” strategy, which utilizes detection algorithms to filter out malicious users before model training to mitigate the influence of the attacks. However, due to the performance limitations of detection algorithms, it is inevitable that malicious users cannot be completely filtered out. To this end, this study proposes a continuous defense method against poisoning attacks on the QoS-aware cloud API recommendation system from a “learning to defense by attacks” perspective with trusted data augmentation. First, this study establishes a defense framework against poisoning attacks based on trusted data augmentation and enhances the robustness of the recommendation system by generating high-quality trusted user data for model training. Second, the study designs a trusted user generation algorithm based on the diffusion model, which employs iterative denoising to learn real-world QoS data distribution related to cloud APIs and generate high-quality trusted user vectors, thus mitigating the influence of data subjected to poisoning attacks on training models. Finally, extensive experiments are conducted based on real-world cloud API QoS datasets, and 11 recommendation algorithms from three categories are utilized to comprehensively evaluate the effectiveness and universality of the proposed defense method. Experimental results indicate that the proposed framework of continuous defense against poisoning attacks based on trusted data augmentation is effective, and the generated trusted user can significantly improve the robustness of the cloud API recommendation system. |
| Key words: recommendation system cloud API poisoning attack continuous defense data augmentation diffusion model |
