SSPT: A Self Supervised Network Traffic Anomaly Detection Method

With the development of network technology, there is a large amount of traffic data in the network at all times. In order to maintain network order and prevent attackers from damaging it, it is necessary to study methods for real-time monitoring of network traffic anomalies. The current machine learning based network traffic anomaly detection methods mostly rely on complex feature dimensionality reduction and feature selection techniques. The model relies on labeled data and is prone to getting stuck in local optima during use. A self supervised patch Transformer(SSPT) based network intrusion detection method is proposed. To solve the problem of wide distribution and strong discreteness of raw attack data, the data is first subjected to single hot encoding and normalization processing, and then segmented into subsets as inputs to the Transformer. Then, training and classification are achieved by sharing the same weights through independent channels. The paper conducted experiments on the CIC-Bell-DNS-EXF-2021 dataset, and the results showed that the proposed method achieved an accuracy of 99.34% for the aforementioned dataset, which is 2.80% higher than the same type of LSTM.