物联网/信息物理系统中的持续后门训练
Continual Backdoor Training in IoT/CPS
AI总结：
本文提出一种针对物联网/信息物理系统中持续学习的后门攻击方法，通过形式化威胁模型、分析持续学习放大后门持久性的原因，并评估不同条件下的攻击效果，揭示了保障终身学习安全的关键挑战。
摘要：
物联网（IoT）和信息物理系统（CPS）越来越依赖持续学习（CL）来适应不断变化的环境、设备异构性和概念漂移，从而提高整体效用。虽然持续适应对于数据模式演变的长期IoT部署至关重要，但它也引入了新的安全漏洞。特别是，后门攻击可以利用增量更新、重放缓冲区和表示重用来植入持久的恶意行为，这些行为在正常操作期间保持休眠，但在特定触发器激活时被触发。在本文中，我们提出了一种针对IoT/CPS系统中持续学习的后门攻击。为此，我们形式化了IoT/CPS特定的威胁模型，分析了为什么持续学习会放大IoT流水线中的后门持久性，并在不同条件下评估了我们的技术。我们的分析强调了在IoT/CPS和工业物联网（IIoT）环境中保障终身学习的关键开放挑战，以及加强安全控制的必要性。
英文摘要：
Internet of Things (IoT) and Cyber-physical systems (CPS) increasingly rely on continual learning (CL) to adapt to evolving environments, device heterogeneity, and concept drift, thereby improving overall utility. While continual adaptation is essential for long-lived IoT deployments where data patterns evolve, it also introduces new security vulnerabilities. In particular, backdoor attacks can exploit incremental updates, replay buffers, and representation reuse to implant persistent malicious behaviors that remain dormant during normal operation but activate upon specific triggers. In this paper, we present a backdoor attack in continual learning used in IoT/CPS systems. To this end, we formalize an IoT/CPS-specific threat model, analyze why continual learning amplifies backdoor persistence in IoT pipelines, and evaluate our technique under varying conditions. Our analysis highlights critical open challenges in securing lifelong learning in IoT/CPS and industrial IoT (IIoT) environments, as well as the need for heightened security controls.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
