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%.
翻译:传统机器学习技术被广泛用于建立信任管理系统,然而,培训数据集的规模可以极大地影响系统的安全性能,而由于缺乏关于新袭击的标签数据,发现恶意节点是一项巨大挑战,因为缺乏关于新袭击的标签数据,因此发现恶意节点是一项巨大挑战。为解决这一问题,本文件为工业无线传感器网络(IWSNs)提供了一个基于基因的对抗网络(GAN)的信托管理机制。首先,采用了第2型模糊逻辑来评价传感器节点的声誉,同时减轻不确定性问题。随后,收集了基于GAN的编码结构,用于进一步检测恶意节点。此外,为了避免因发现错误而永久脱离网络的正常节点,为增强信任管理的复原力,建立了基于GAN的信任救赎模式。根据最新的检测结果,开发了一种适应动态工业环境的信任模式更新方法。拟议的信任管理机制最终用于确保可靠和实时数据传输的集群,并模拟结果显示它达到高达96%的检测率,作为低正率率率,低于8。