# Machine Learning in IoT Security: Current Solutions and Future Challenges

arXiv: https://arxiv.org/abs/1904.05735 · HTML: https://mlsanity.com/paper/1904.05735/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2019.

## Built on by (most cited first, 25 of 25)

- [IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective](https://mlsanity.com/paper/2209.08018/) (2022)
- [Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction](https://mlsanity.com/paper/2401.12262/) (2024)
- [Unsupervised Deep Learning for IoT Time Series](https://mlsanity.com/paper/2302.03284/) (2023)
- [An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks](https://mlsanity.com/paper/2101.12270/) (2021)
- [Machine Learning for Security in Vehicular Networks: A Comprehensive Survey](https://mlsanity.com/paper/2105.15035/) (2021)
- [A Practical Cross-Device Federated Learning Framework over 5G Networks](https://mlsanity.com/paper/2204.08134/) (2022)
- [IoTDevID: A Behavior-Based Device Identification Method for the IoT](https://mlsanity.com/paper/2102.08866/) (2021)
- [Modulation and Coding for NOMA and RSMA](https://mlsanity.com/paper/2410.00239/) (2024)
- [Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey](https://mlsanity.com/paper/2310.10665/) (2023)
- [Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2210.13547/) (2022)
- [Large Language Model-driven Security Assistant for Internet of Things via Chain-of-Thought](https://mlsanity.com/paper/2505.06307/) (2025)
- [IoT Botnet Detection Using an Economic Deep Learning Model](https://mlsanity.com/paper/2302.02013/) (2023)
- [Generative AI for Internet of Things Security: Challenges and Opportunities](https://mlsanity.com/paper/2502.08886/) (2025)
- [A Low-Cost Multi-Agent System for Physical Security in Smart Buildings](https://mlsanity.com/paper/2209.00741/) (2022)
- [Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning](https://mlsanity.com/paper/2301.03532/) (2023)
- [IoT Data Trust Evaluation via Machine Learning](https://mlsanity.com/paper/2308.11638/) (2023)
- [Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation](https://mlsanity.com/paper/2402.11760/) (2024)
- [Securing the Future: Proactive Threat Hunting for Sustainable IoT Ecosystems](https://mlsanity.com/paper/2406.14804/) (2024)
- [Weaponizing Language Models for Cybersecurity Offensive Operations: Automating Vulnerability Assessment Report Validation; A Review Paper](https://mlsanity.com/paper/2505.04265/) (2025)
- [Practical and Private Hybrid ML Inference with Fully Homomorphic Encryption](https://mlsanity.com/paper/2509.01253/) (2025)
- [IoT Device Identification with Machine Learning: Common Pitfalls and Best Practices](https://mlsanity.com/paper/2601.20548/) (2026)
- [Exploring Robust Intrusion Detection: A Benchmark Study of Feature Transferability in IoT Botnet Attack Detection](https://mlsanity.com/paper/2602.23874/) (2026)
- [Dimensionality Reduction for Cyberattack Classification: A Comparative Evaluation of PCA and Linear Predictive Coding](https://mlsanity.com/paper/2606.05584/) (2026)
- [F-ACVAE: A Federated Adaptive Conditional Variational Auto-Encoder for Privacy-Preserving Intrusion Detection in IoT Networks](https://mlsanity.com/paper/2607.04698/) (2026)
- [SIDE: Sensor Impersonation Detection at the Edge via Sequence Prediction](https://mlsanity.com/paper/2609.06271/) (2026)

## Abstract (arXiv)

The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML- and DL-based IoT security.