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Intrusion Detection Method Based on Wasserstein Generative Adversarial Network

An intrusion detection method based on Wasserstein generative adversarial network and deep neural network was proposed to solve the problem of low accuracy and high false positive rate caused by data imbalance in traditional intrusion detection system based on machine learning. By learning the distribution of the original data set and generating new samples, the method can improve the class imbalance of the data set, so as to train the intrusion detection model and improve the detection efficiency of the model. First, the training set is balanced by combining Wasserstein GAN of the Dropout regular network to generate new data. Then the balanced data set is used to train DNN, and the DNN model obtained after training is used for intrusion detection. Experimental results on NSL-KDD data set show that this method is more effective than the traditional method.