Botnet detection based on generative adversarial network - Futai ZOU; Yue TAN; Lin WANG; Yongkang JIANG (Tongxin xuebao)
In order to solve the problems of botnets’ strong concealment and difficulty in identification, and improve the detection accuracy of botnets, a botnet detection method based on generative adversarial networks was proposed.By reorganizing the data packets in the botnet traffic into streams, the traffic statistics characteristics in the time dimension and the traffic image characteristics in the space dimension were extracted respectively.Then with the botnet traffic feature generation algorithm based on generative adversarial network, botnet feature samples were produced in the two dimensions.Finally combined with the application of deep learning in botnet detection scenarios, a botnet detection model based on DCGAN and a botnet detection model based on BiLSTM-GAN were proposed.Experiments show that the proposed model improves the botnet detection ability and generalization ability.