针对雾计算资源调度中的模型欺骗攻击，提出主动加固方法提升系统稳定性。
Mitigating Evasion Attacks in Fog Computing Resource Provisioning Through Proactive Hardening
- 通过对抗训练增强在线分类器的鲁棒性，预防模型被逆向和欺骗。
- 实验表明该方法能有效维持资源调度系统的稳定，抵御攻击干扰。
- 适合关注雾计算安全与机器学习防御的研究者参考。
本文研究了在雾网络资源调度中，基于k-means算法分配虚拟机时面临的模型完整性攻击风险。该算法分两阶段迭代运行：离线聚类生成工作负载集群，线上分类将新请求分配至已有集群。首先，威胁方通过基于查询的逆向工程探测机器学习模型（即聚类方案）；随后在离线阶段发动被动因果型（欺骗）攻击。为防御此类攻击，本文提出一种主动防护方法，利用对抗训练提升分类器的抗攻击能力。结果表明，所提技术可有效维持资源调度系统的稳定性，抵抗各类攻击。
原文摘要 · Abstract (English)
This paper investigates the susceptibility to model integrity attacks that overload virtual machines assigned by the k-means algorithm used for resource provisioning in fog networks. The considered k-means algorithm runs two phases iteratively: offline clustering to form clusters of requested workload and online classification of new incoming requests into offline-created clusters. First, we consider an evasion attack against the classifier in the online phase. A threat actor launches an exploratory attack using query-based reverse engineering to discover the Machine Learning (ML) model (the clustering scheme). Then, a passive causative (evasion) attack is triggered in the offline phase. To defend the model, we suggest a proactive method using adversarial training to introduce attack robustness into the classifier. Our results show that our mitigation technique effectively maintains the stability of the resource provisioning system against attacks.
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