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%.

本文提出了一种基于生成对抗网络（GAN）的信任管理机制，用来解决对新型攻击的标记数据缺失以及传统机器学习算法训练数据集大小对系统安全性能的影响问题，在工业无线传感器网络中采用了二阶模糊逻辑来评估传感器节点的信誉，并建立了基于 GAN 的编解码结构来收集信任向量和检测恶意节点，同时构建了基于 GAN 的信任赎回模型来提高信任管理的鲁棒性。该机制可应用于可靠实时数据传输的安全聚类，实验结果表明具有高达 96% 的检测率和低于 8% 的误报率。