  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%.
