mlsanityData to 14 Oct 2026

Continual Backdoor Training in IoT/CPS

arXiv 2606.14987 ↗First posted 12 Jun 2026cs.CR cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 12 Jun 2026.

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14 of 28 references.

References · 14

  1. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  2. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,333
  3. 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,106
  4. 2018How To Backdoor Federated Learningcited by 765
  5. 2019Hidden Trigger Backdoor Attackscited by 371
  6. 2021Co2L: Contrastive Continual Learningcited by 244
  7. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 177
  8. 2021Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacycited by 14
  9. 2025PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environmentscited by 9
  10. 2025Dynamic User-controllable Privacy-preserving Few-shot Sensing Frameworkcited by 8
  11. 2025Dynamic Black-box Backdoor Attacks on IoT Sensory Datacited by 8
  12. 2025Log Anomaly Detection with Large Language Models via Knowledge-Enriched Fusioncited by 8
2 more
  1. 2026Contrastive Continual Learning for Model Adaptability in Internet of Thingscited by 4
  2. 2023Adversary Aware Continual Learningcited by 3

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  1. 2026Backdoor Attacks on Contrastive Continual Learning for IoT Systems
  2. 2024Persistent Backdoor Attacks in Continual Learning
  3. 2024Attack On Prompt: Backdoor Attack in Prompt-Based Continual Learning
  4. 2026Robust Dynamic Expansion for Continual Learning under Backdoor Attacks via Purification and Selective Recovery
  5. 2024Flatness-aware Sequential Learning Generates Resilient Backdoors
  6. 2022False Memory Formation in Continual Learners Through Imperceptible Backdoor Trigger
  7. 2024Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning
  8. 2026Unlearning to Protect: A Distilled Reinforcement Learning Framework with Privacy-Preserving Feature Unlearning and XAI for IoT Security