mlsanityData to 14 Oct 2026

IoT Security Techniques Based on Machine Learning

arXiv 1801.06275 ↗First posted 19 Jan 2018cs.CRcited by 22

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Abstract (arXiv)

Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning. We focus on the machine learning based IoT authentication, access control, secure offloading and malware detection schemes to protect data privacy. In this article, we discuss the challenges that need to be addressed to implement these machine learning based security schemes in practical IoT systems.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Jan 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 6; 2023: 7; 2024: 1; 2025: 1; 2026: 1.

22 papers.

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  1. 2023Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimizationcited by 64
  2. 2023Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examplescited by 43
  3. 2021Reinforcement Learning for IoT Security: A Comprehensive Surveycited by 23
  4. 2021Reinforcement Learning for Feedback-Enabled Cyber Resiliencecited by 19
  5. 2022Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoTcited by 12
  6. 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestrationcited by 12
  7. 2022ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learningcited by 11
  8. 2021Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPScited by 10
  9. 2021A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methodscited by 10
  10. 2023Differentially Private Video Activity Recognitioncited by 9
  11. 2024Integrating Large Language Models with Internet of Things Applicationscited by 7
  12. 2023Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generationcited by 6
10 more
  1. 2021SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine Learningcited by 4
  2. 2022NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domainscited by 3
  3. 2022Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directionscited by 2
  4. 2023Ensemble Learning based Anomaly Detection for IoT Cybersecurity via Bayesian Hyperparameters Sensitivity Analysiscited by 2
  5. 2022Unsupervised Ensemble Based Deep Learning Approach for Attack Detection in IoT Networkcited by 1
  6. 2023IoT Device Identification Based on Network Communication Analysis Using Deep Learningcited by 1
  7. 2025FedAPM: Federated Learning via ADMM with Partial Model Personalizationcited by 1
  8. 2021Towards Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Methodcited by 0
  9. 2023An AI-Enabled Framework to Defend Ingenious MDT-based Attacks on the Emerging Zero Touch Cellular Networkscited by 0
  10. 2026Para-Pipe: Exploiting Hierarchical Operator Parallelism of ML Computational Graphs on SoCscited by 0