Authentication Mechanism Based on Physical Layer Security in Industrial Wireless Sensor Networks

When wireless network technology is applied to industrial scenes, the open channel environment makes industrial equipment more vulnerable to attacks and threats from illegal nodes, such as eavesdropping, deception and identity information forgery. The complexity and variety of attack methods make the supervised machine learning algorithm insufficient to establish a low complexity, lightweight and high security authentication mechanism in industrial wireless sensor networks. Moreover, the wireless electromagnetic wave will be affected by additive noise and fading in the propagation process, making the wireless channel in a dynamic state. Based on this, we study a new authentication mechanism based on physical layer security for wireless sensor networks in dynamic industrial scenarios. Using more precise physical layer channel information, and building an authentication model around positive-unlabeled (PU) learning and bootstrap aggregating (bagging) strategy, we can accurately distinguish legal nodes and illegal nodes in the received channel information in the industrial scene where only the channel information of legal nodes is known. Finally, the effectiveness of the scheme is verified by using the public data set collected by the national institute of standards and technology (NIST) in a real industrial scene.