Abstract: Artificial intelligence technology has shown great potential in the field of network traffic classification and has had a profound impact on the strategic landscape of cyberspace security.But some studies have also found that deep learning models have serious vulnerabilities,and adversarial samples derived from this vulnerability can significantly reduce the accuracy of model detection.At present,adversarial samples are widely and deeply studied in the field of image classification,and are still in the development stage in the field of network traffic classification.The existing adversa-rial network traffic technology is only effective for specific samples,and has high time cost and low practicality.Therefore,this paper proposes a method for generating adversarialnetwork traffic based on general perturbations.It uses the properties of spatial feature distribution to find a general perturbation vector,adds this perturbation to normal traffic to generate adversarial network traffic,and causes a high probability of detection errors in the network traffic classifier.Compared with existing methods,this paper conducts experimental tests on Moore dataset and ISCX2016 dataset.The results show that under the same conditions,this method is effective for generating adversarial network traffic attack classifiers for all samples on Moore dataset and ISCX2016 dataset,with a success rate of over 80%.It can effectively attack different classifiers,with model transferability effect.At the same time,the time cost is less than 1 ms,achieving rapid generation of adversarial network traffic with much higher efficiency than existing methods.

Key words: Deep learning, Network traffic classification, Adversarial network traffic, Universal perturbations