论文标题
在工业无线传感器网络中，可信赖和安全的聚类的生成对手学习
Generative Adversarial Learning for Trusted and Secure Clustering in Industrial Wireless Sensor Networks
论文作者
论文摘要
传统的机器学习技术已被广泛用于建立信任管理系统。但是，训练数据集的规模可以显着影响系统的安全性能，而由于缺乏有关新颖攻击的标签数据，检测恶意节点是一个巨大的挑战。为了解决这个问题，本文介绍了基于工业无线传感器网络（IWSN）的基于生成的对抗网络（GAN）的信任管理机制。首先，采用2型模糊逻辑来评估传感器节点的声誉，同时减轻不确定性问题。然后，收集信任向量以训练基于GAN的编解码器结构，该结构用于进一步的恶意节点检测。此外，为了避免由于错误检测而永久地与网络隔离的正常节点，构建了基于GAN的信任救赎模型，以增强信任管理的弹性。基于最新检测结果，开发了一种信任模型更新方法，以适应动态工业环境。最终将提出的信任管理机制应用于可靠和实时数据传输的确保聚类，并且模拟结果表明，它达到了高达96％的高检测率，以及低于8％的较低的假阳性率。
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%.
