# A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

arXiv: https://arxiv.org/abs/1807.11023 · HTML: https://mlsanity.com/paper/1807.11023/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2018.

## Built on by (most cited first, 30 of 42)

- [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022)
- [NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation](https://mlsanity.com/paper/2305.00097/) (2023)
- [Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation](https://mlsanity.com/paper/2105.04184/) (2021)
- [Unsupervised Deep Learning for IoT Time Series](https://mlsanity.com/paper/2302.03284/) (2023)
- [Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2404.06114/) (2024)
- [Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey](https://mlsanity.com/paper/2101.10181/) (2021)
- [Machine Learning for Security in Vehicular Networks: A Comprehensive Survey](https://mlsanity.com/paper/2105.15035/) (2021)
- [Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats](https://mlsanity.com/paper/2204.03433/) (2022)
- [A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainability](https://mlsanity.com/paper/2310.17944/) (2023)
- [Securing the Digital World: Protecting smart infrastructures and digital industries with Artificial Intelligence (AI)-enabled malware and intrusion detection](https://mlsanity.com/paper/2401.01342/) (2023)
- [The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks](https://mlsanity.com/paper/2107.12433/) (2021)
- [IoTDevID: A Behavior-Based Device Identification Method for the IoT](https://mlsanity.com/paper/2102.08866/) (2021)
- [Artificial Neural Network for Cybersecurity: A Comprehensive Review](https://mlsanity.com/paper/2107.01185/) (2021)
- [Latent Variable Models in the Era of Industrial Big Data: Extension and Beyond](https://mlsanity.com/paper/2208.10847/) (2022)
- [ChatIoT: Large Language Model-based Security Assistant for Internet of Things with Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.09896/) (2025)
- [Generative Adversarial Learning for Trusted and Secure Clustering in Industrial Wireless Sensor Networks](https://mlsanity.com/paper/2210.07707/) (2022)
- [Utility Fairness for the Differentially Private Federated Learning](https://mlsanity.com/paper/2109.05267/) (2021)
- [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)
- [Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey](https://mlsanity.com/paper/2406.08115/) (2024)
- [Impact of Missing Values in Machine Learning: A Comprehensive Analysis](https://mlsanity.com/paper/2410.08295/) (2024)
- [Large Language Model-driven Security Assistant for Internet of Things via Chain-of-Thought](https://mlsanity.com/paper/2505.06307/) (2025)
- [Federated Learning Cost Disparity for IoT Devices](https://mlsanity.com/paper/2204.08036/) (2022)
- [Intrusion Detection in Internet of Things using Convolutional Neural Networks](https://mlsanity.com/paper/2211.10062/) (2022)
- [A Review of Cybersecurity Incidents in the Food and Agriculture Sector](https://mlsanity.com/paper/2403.08036/) (2024)
- [Breaking Resource Barriers in Speech Emotion Recognition via Data Distillation](https://mlsanity.com/paper/2406.15119/) (2024)
- [Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges](https://mlsanity.com/paper/2410.13894/) (2024)
- [Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning](https://mlsanity.com/paper/2412.21164/) (2024)
- [Sensing and Mapping for Better Roads: Initial Plan for Using Federated Learning and Implementing a Digital Twin to Identify the Road Conditions in a Developing Country -- Sri Lanka](https://mlsanity.com/paper/2107.14551/) (2021)
- [Secure solutions for Smart City Command Control Centre using AIOT](https://mlsanity.com/paper/2108.00003/) (2021)

## Abstract (arXiv)

The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT system effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML /DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.