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

TL;DR

This paper introduces a GAN-based trust management system for Industrial Wireless Sensor Networks that enhances malicious node detection and trust resilience, achieving high detection accuracy and low false positives in dynamic industrial environments.

Contribution

It proposes a novel GAN-based trust management framework incorporating fuzzy logic and trust redemption to improve security and resilience in IWSNs.

Findings

Detection rate up to 96%

False positive rate below 8%

Effective in dynamic environments

Abstract

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%.