Research on machine learning-based device fingerprint recognition technology for power internet of things

Abstract With the rapid development of Electric Power Internet of Things (IoT) technology, a large number of devices are being networked and exposed to cyberspace. Due to the disparity in security design levels and lax management during usage, electric power terminals are susceptible targets for network attackers. This not only causes losses to device owners but also poses a threat to the overall cybersecurity of the network, as compromised devices can serve as nodes for botnets. The importance of addressing this issue cannot be underestimated. Asset identification is a prerequisite for the secure management of IoT devices. This paper analyzes and studies existing asset identification technologies. Existing device identification methods based on message content features have problems such as reliance on textual features of message content and difficulties in labeling large-scale data. To overcome these limitations, a machine learning-based classification method for IoT devices is proposed. This method extracts features from the web homepage of devices and generates feature vector fingerprints. By leveraging the random forest algorithm, the accuracy of IoT device classification is improved. This approach is suitable for asset identification of IoT devices and provides support for the precise implementation of vulnerability scanning for IoT devices.