# IoT Security Techniques Based on Machine Learning

arXiv: https://arxiv.org/abs/1801.06275 · HTML: https://mlsanity.com/paper/1801.06275/

## Key facts

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

## Built on by (most cited first, 22 of 22)

- [Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimization](https://mlsanity.com/paper/2304.11823/) (2023)
- [Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples](https://mlsanity.com/paper/2307.10562/) (2023)
- [Reinforcement Learning for IoT Security: A Comprehensive Survey](https://mlsanity.com/paper/2102.07247/) (2021)
- [Reinforcement Learning for Feedback-Enabled Cyber Resilience](https://mlsanity.com/paper/2107.00783/) (2021)
- [Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT](https://mlsanity.com/paper/2202.07391/) (2022)
- [Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration](https://mlsanity.com/paper/2205.01423/) (2022)
- [ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning](https://mlsanity.com/paper/2208.12897/) (2022)
- [Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS](https://mlsanity.com/paper/2102.07244/) (2021)
- [A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods](https://mlsanity.com/paper/2110.07832/) (2021)
- [Differentially Private Video Activity Recognition](https://mlsanity.com/paper/2306.15742/) (2023)
- [Integrating Large Language Models with Internet of Things Applications](https://mlsanity.com/paper/2410.19223/) (2024)
- [Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation](https://mlsanity.com/paper/2305.11039/) (2023)
- [SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine Learning](https://mlsanity.com/paper/2101.02780/) (2021)
- [NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domains](https://mlsanity.com/paper/2207.10803/) (2022)
- [Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2210.13547/) (2022)
- [Ensemble Learning based Anomaly Detection for IoT Cybersecurity via Bayesian Hyperparameters Sensitivity Analysis](https://mlsanity.com/paper/2307.10596/) (2023)
- [Unsupervised Ensemble Based Deep Learning Approach for Attack Detection in IoT Network](https://mlsanity.com/paper/2207.07903/) (2022)
- [IoT Device Identification Based on Network Communication Analysis Using Deep Learning](https://mlsanity.com/paper/2303.12800/) (2023)
- [FedAPM: Federated Learning via ADMM with Partial Model Personalization](https://mlsanity.com/paper/2506.04672/) (2025)
- [Towards Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Method](https://mlsanity.com/paper/2111.10055/) (2021)
- [An AI-Enabled Framework to Defend Ingenious MDT-based Attacks on the Emerging Zero Touch Cellular Networks](https://mlsanity.com/paper/2308.02923/) (2023)
- [Para-Pipe: Exploiting Hierarchical Operator Parallelism of ML Computational Graphs on SoCs](https://mlsanity.com/paper/2609.04168/) (2026)

## 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.