Network Device Detection Method Based on Device Time-Delay and Hybrid Deep Learning Model

Current network device identification methods based on hardware fingerprints are not efficient in collecting and extracting features, and device classification methods based on traffic characteristics only consider existing device types and cannot detect abnormal devices. To address these problems, this study proposes a method that extracts the processing time-delay feature of network device based on Global Navigation Satellite System (GNSS) high-precision timing technology. A Bayesian convolutional autoencoder model, called BCNN-AE, is constructed to efficiently identify known types and detect unknown types: the model includes feature extraction, feature reconstruction, and composite prediction modules. First, the proposed method uses GNSS high-precision timing technology to achieve nanosecond-level measurement of network traffic processing time-delays and constructs a device time-delay distribution feature vector. Next, the feature extraction module uses Bayesian convolution to extract time-delay distribution features, and the feature reconstruction module uses an Autoencoder (AE) to learn a compressed reconstruction representation of the time-delay vector. Finally, the composite prediction module makes a comprehensive judgment based on uncertainty and reconstruction error thresholds to identify known types and detect unknown/abnormal device types. Experiments conducted on a dataset collected in a laboratory simulation environment and a public dataset Aalto show that the use of device time-delays can accurately represent different network device types. The results show that the proposed method achieves higher recognition accuracy than that of the baseline model and can effectively detect unknown/abnormal device types.