Anomaly traffic detection based on feature fluctuation for secure industrial internet of things

The detection of anomaly traffic in internet of things (IoT) is mainly based on the original binary data at the traffic packet level and the structured data at the session flow level. This kind of dataset has a single feature extraction method and relies on prior manual knowledge. It is easy to lose critical information during data processing, which reduces the validity and robustness of the dataset. In this paper, we first construct a new anomaly traffic dataset based on the traffic packet and session flow data in the Iot-23 dataset. Second, we propose a feature extraction method based on feature fluctuation. Our proposed method can effectively solve the disadvantage that the data collected in different scenarios have different characteristics, which leads to the feature containing less information. Compared with the traditional anomaly traffic detection model, experiments show that our proposed method based on feature fluctuation has stronger robustness, can improve the accuracy of anomaly traffic detection and the generalization ability of the traditional model, and is more conducive to the detection of anomalous traffic in IoT.