# Generative Adversarial Learning for Trusted and Secure Clustering in Industrial Wireless Sensor Networks

arXiv: https://arxiv.org/abs/2210.07707 · HTML: https://mlsanity.com/paper/2210.07707/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 14 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security](https://mlsanity.com/paper/1807.11023/) (2018)
- [Towards Real-Time Advancement of Underwater Visual Quality with GAN](https://mlsanity.com/paper/1712.00736/) (2017)
- [SCH-GAN: Semi-supervised Cross-modal Hashing by Generative Adversarial Network](https://mlsanity.com/paper/1802.02488/) (2018)
- [Recommendations on Designing Practical Interval Type-2 Fuzzy Systems](https://mlsanity.com/paper/1907.01697/) (2019)
- [A Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Industrial Wireless Sensor Networks](https://mlsanity.com/paper/2207.09936/) (2022)
- [An Evolutionary Game based Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Wireless Sensor Networks](https://mlsanity.com/paper/2207.10282/) (2022)

## Built on by (most cited first, 3 of 3)

- [Generative AI for Physical Layer Communications: A Survey](https://mlsanity.com/paper/2312.05594/) (2023)
- [Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges](https://mlsanity.com/paper/2405.17454/) (2024)
- [Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Survey](https://mlsanity.com/paper/2505.07882/) (2025)

## Abstract (arXiv)

Traditional machine learning techniques have been widely used to establish the trust management systems. However, the scale of training dataset can significantly affect the security performances of the systems, while it is a great challenge to detect malicious nodes due to the absence of labeled data regarding novel attacks. To address this issue, this paper presents a generative adversarial network (GAN) based trust management mechanism for Industrial Wireless Sensor Networks (IWSNs). First, type-2 fuzzy logic is adopted to evaluate the reputation of sensor nodes while alleviating the uncertainty problem. Then, trust vectors are collected to train a GAN-based codec structure, which is used for further malicious node detection. Moreover, to avoid normal nodes being isolated from the network permanently due to error detections, a GAN-based trust redemption model is constructed to enhance the resilience of trust management. Based on the latest detection results, a trust model update method is developed to adapt to the dynamic industrial environment. The proposed trust management mechanism is finally applied to secure clustering for reliable and real-time data transmission, and simulation results show that it achieves a high detection rate up to 96%, as well as a low false positive rate below 8%.