# Active Learning for Wireless IoT Intrusion Detection

arXiv: https://arxiv.org/abs/1808.01412 · HTML: https://mlsanity.com/paper/1808.01412/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2018.

## Built on by (most cited first, 19 of 19)

- [Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection](https://mlsanity.com/paper/2105.10500/) (2021)
- [SoK: The Impact of Unlabelled Data in Cyberthreat Detection](https://mlsanity.com/paper/2205.08944/) (2022)
- [Unsupervised Deep Learning for IoT Time Series](https://mlsanity.com/paper/2302.03284/) (2023)
- [Active Sensing for Communications by Learning](https://mlsanity.com/paper/2112.04075/) (2021)
- [Machine Learning in NextG Networks via Generative Adversarial Networks](https://mlsanity.com/paper/2203.04453/) (2022)
- [Active Learning for Network Traffic Classification: A Technical Study](https://mlsanity.com/paper/2106.06933/) (2021)
- [Deep Active Learning for Text Classification with Diverse Interpretations](https://mlsanity.com/paper/2108.10687/) (2021)
- [Network Support for High-performance Distributed Machine Learning](https://mlsanity.com/paper/2102.03394/) (2021)
- [Learning from the Best: Active Learning for Wireless Communications](https://mlsanity.com/paper/2402.04896/) (2024)
- [Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning](https://mlsanity.com/paper/2505.19263/) (2025)
- [Cellular Traffic Prediction via Deep State Space Models with Attention Mechanism](https://mlsanity.com/paper/2506.15688/) (2025)
- [Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization](https://mlsanity.com/paper/2108.00785/) (2021)
- [Jasmine: A New Active Learning Approach to Combat Cybercrime](https://mlsanity.com/paper/2108.06238/) (2021)
- [AI Empowered Net-RCA for 6G](https://mlsanity.com/paper/2212.00331/) (2022)
- [MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Prediction](https://mlsanity.com/paper/2505.21553/) (2025)
- [Data Games: A Game-Theoretic Approach to Swarm Robotic Data Collection](https://mlsanity.com/paper/2303.03602/) (2023)
- [Robust Group Anomaly Detection for Quasi-Periodic Network Time Series](https://mlsanity.com/paper/2506.16815/) (2025)
- [LGTBIDS: Layer-wise Graph Theory Based Intrusion Detection System in Beyond 5G](https://mlsanity.com/paper/2210.03518/) (2022)
- [TableNet A Large-Scale Table Dataset with LLM-Powered Autonomous](https://mlsanity.com/paper/2604.13041/) (2026)

## Abstract (arXiv)

Internet of Things (IoT) is becoming truly ubiquitous in our everyday life, but it also faces unique security challenges. Intrusion detection is critical for the security and safety of a wireless IoT network. This paper discusses the human-in-the-loop active learning approach for wireless intrusion detection. We first present the fundamental challenges against the design of a successful Intrusion Detection System (IDS) for wireless IoT network. We then briefly review the rudimentary concepts of active learning and propose its employment in the diverse applications of wireless intrusion detection. Experimental example is also presented to show the significant performance improvement of the active learning method over traditional supervised learning approach. While machine learning techniques have been widely employed for intrusion detection, the application of human-in-the-loop machine learning that leverages both machine and human intelligence to intrusion detection of IoT is still in its infancy. We hope this article can assist the readers in understanding the key concepts of active learning and spur further research in this area.