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.

翻译：物联网和信息物理系统日益依赖持续学习以适应不断变化的环境、设备异质性及概念漂移，从而提升整体效用。虽然持续适应对于数据模式不断演变的长期物联网部署至关重要，但也引入了新的安全漏洞。具体而言，后门攻击可利用增量更新、重放缓冲区和表示重用机制植入持久性恶意行为，这些行为在正常操作期间保持潜伏状态，但在特定触发器激活时被触发。本文提出了一种面向物联网/信息物理系统持续学习场景的后门攻击方法。为此，我们形式化定义了物联网/信息物理系统特定的威胁模型，分析了持续学习为何会加剧物联网流水线中后门的持久性，并在不同条件下评估了所提技术。我们的分析揭示了在物联网/信息物理系统及工业物联网环境中保障终身学习安全的关键开放挑战，并强调了加强安全控制的必要性。