With the explosive development of mobile Internet and deep learning (DL), intelligent edge computing services based on collaborative learning are widely deployed in various application scenarios. These intelligent services include intelligent applications based on edge computing and DL-based optimization for edge computing (e.g., caching and communicating). However, in a wide variety of domains, DL has been found to be vulnerable to adversarial attacks, especially architecture-independent backdoor attacks. It embeds the attack pattern into the learned model and only performs the attack when it encounters the corresponding trigger. In this article, for the first time we analyze the impact of backdoor attacks on intelligent edge computing services. The simulation results demonstrate that once one or more edge nodes implement backdoor attacks, the embedded attack pattern will rapidly expand to all relevant edge nodes, which poses huge challenges to security-sensitive intelligent edge computing services. Subsequently, we analyze the trade-off between expected performance and ability to defend against backdoor attacks, which sheds new light on designing defense mechanisms for intelligent edge computing services. To address the challenges posed by backdoor attacks, we propose a stability-based defense mechanism. The experimental results demonstrate that the newly proposed defense mechanism can effectively defend against different levels of backdoor attacks without knowing whether there are adversaries, which is conducive to the deployment of the stability-based defense mechanism in real-world scenarios.

中文翻译：

---

边缘计算服务的稳定性分析与后门攻击防御


随着移动互联网和深度学习的爆发式发展，基于协作学习的智能边缘计算服务广泛部署在各种应用场景中。这些智能服务包括基于边缘计算的智能应用和基于深度学习的边缘计算优化（例如缓存和通信）。然而，在许多领域中，人们发现深度学习很容易受到对抗性攻击，尤其是与架构无关的后门攻击。它将攻击模式嵌入到学习的模型中，并且仅在遇到相应的触发器时才执行攻击。在本文中，我们首次分析了后门攻击对智能边缘计算服务的影响。仿真结果表明，一旦一个或多个边缘节点实施后门攻击，嵌入式攻击模式将迅速扩展到所有相关边缘节点，这对安全敏感的智能边缘计算服务带来巨大挑战。随后，我们分析了预期性能和防御后门攻击的能力之间的权衡，这为设计智能边缘计算服务的防御机制提供了新的思路。为了应对后门攻击带来的挑战，我们提出了一种基于稳定性的防御机制。实验结果表明，新提出的防御机制可以在不知道是否存在对手的情况下有效防御不同级别的后门攻击，这有利于基于稳定性的防御机制在现实场景中的部署。