Target adversarial sample generation for malicious traffic classification model

In the face of attacks against sample, machine learning technique is weak, the current flow against malicious classification samples against attack algorithm is numerous, but the vast majority of algorithms only to a certain classification model for design, migration attack ability is low, and is targeted, the target structure against samples as long as can let classification model classification error, The target attack has a clear directivity, which can mislead the model to classify as the designated category of the attacker. Therefore, in real scenes, the target attack will often bring greater threat, so it has more realistic significance. Based on Generative Adversarial Network (GAN), this paper proposes a method of generating target black-box Adversarial samples with norm limited disturbances. The shadow classifier is used to fit multiple Network traffic classification models as the discriminator and training generator. Generating and adding a specific disturbance that does not change the attack characteristics to a certain type of original malicious traffic, thus generating an adversarial sample that can attack multiple malicious traffic at the same time and misclassify it into a specified category by the classification model. The adversarial sample has strong target and good mobility.