物联网系统中对比持续学习的后门攻击
Backdoor Attacks on Contrastive Continual Learning for IoT Systems
AI总结：
本文研究了物联网系统中对比持续学习的后门攻击问题，分析了攻击机制并提出针对性的防御策略。
摘要：
物联网系统越来越多地依赖持续学习来适应非平稳环境。这些环境可能包括传感器漂移、用户行为变化、设备老化和对抗性动态等因素。对比持续学习（CCL）结合了对比表征学习与增量适应，使模型能够在不同任务和领域之间实现鲁棒的特征复用。然而，对比目标的几何特性，当与基于重放的复习和稳定性保持正则化相结合时，会引入新的安全漏洞。值得注意的是，后门攻击可以利用嵌入对齐和重放强化，从而植入持久的恶意行为，使其在更新和部署周期中持续存在。本文对物联网系统中CCL的后门攻击进行了全面分析。我们正式化了嵌入层攻击的目标，检查了物联网部署中独特的持久性机制，并开发了针对物联网的分层分类体系。此外，我们比较了不同学习范式中的漏洞，并在物联网约束下评估了防御策略，包括有限内存、边缘计算和联邦聚合。我们的发现表明，虽然CCL在增强适应性物联网智能方面效果显著，但如果未得到充分保护，它也可能提高长期的表征层面威胁。
英文摘要：
The Internet of Things (IoT) systems increasingly depend on continual learning to adapt to non-stationary environments. These environments can include factors such as sensor drift, changing user behavior, device aging, and adversarial dynamics. Contrastive continual learning (CCL) combines contrastive representation learning with incremental adaptation, enabling robust feature reuse across tasks and domains. However, the geometric nature of contrastive objectives, when paired with replay-based rehearsal and stability-preserving regularization, introduces new security vulnerabilities. Notably, backdoor attacks can exploit embedding alignment and replay reinforcement, enabling the implantation of persistent malicious behaviors that endure through updates and deployment cycles. This paper provides a comprehensive analysis of backdoor attacks on CCL within IoT systems. We formalize the objectives of embedding-level attacks, examine persistence mechanisms unique to IoT deployments, and develop a layered taxonomy tailored to IoT. Additionally, we compare vulnerabilities across various learning paradigms and evaluate defense strategies under IoT constraints, including limited memory, edge computing, and federated aggregation. Our findings indicate that while CCL is effective for enhancing adaptive IoT intelligence, it may also elevate long-lived representation-level threats if not adequately secured.
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