In recent years, the Internet of Things (IoT) has penetrated all aspects of our lives through smart cities, health, industries and others that are related to people's livelihood. With the increasing number of IoT devices, more and more personal information is exposed in the network space, which inevitably brings some network security problems. Due to the diversity and heterogeneity of IoT devices, identification of such devices in the complex IoT environments remains a major challenge. Existing deep learning-based device identification methods achieve identification of IoT devices by automatically extracting device traffic features, but usually only single modal features of device traffic are considered, which cannot achieve all-around characterization features of communication traffic and affect the identification results. Therefore, we propose an identification method, termed DMRMTT, that employs a Deep convolutional maxout network and MTT model (Multiple Time-series Transformers) to automatically extract the spatial and temporal features of IoT communication session fingerprints and perform further fusion using the structure of the residual, which makes up for the limitations of the existing methods for studying device traffic. This method can improve the characterization of device traffic behaviour and achieve a more accurate identification of IoT devices. Its efficacy is experimentally validated by using two publicly availbale datasets and compared with existing methods. Results show that our method outperforms other methods in widely used performance metrics and achieves 99.82% identification accuracy, demonstrating its superiority and usefulness in IoT device identification.

As a bridge for information interaction between people and things, and things and things, IoT devices bring security issues and data privacy protection issues that have always been the main challenges in the IoT environment. In terms of abnormal traffic detection of IoT devices, data sharing between device data is usually not possible. This makes the deep learning method for model training based on a large amount of data unable to fully exert its strength due to the lack of IoT device attack instances, resulting in the problem of low detection accuracy. To this end, we propose an abnormal traffic detection model for IoT devices, FL-DSCNN (Federated Learning and Depthwise separable convolutional neural networks). First, the mayfly optimization algorithm is used to select the traffic features, and the model training time is reduced by reducing the feature dimension. Then, by introducing the FL framework, the depthwise separable convolutional neural network is used as a local model for collaborative training without sharing private data, avoiding the problem of lack of labeled data due to the “data silos” phenomenon while protecting data privacy. In addition, we experimentally verify the proposed method on the existing public dataset Aposemat IoT-23 dataset and compare and evaluate it with existing methods. The experimental results show that the method can achieve two-class and multi-class detection respectively. The detection accuracy rates of 98.52% and 97.73% prove the progress and superiority of the proposed FL-DSCNN model in the detection of abnormal traffic of IoT devices.