MMD Fence GAN Unsupervised Anomaly Detection Model Based on Maximum Mean Discrepancy

Abstract

Due to data imbalances and the complexity of data, identifying outliers can be challenging. To address this issue, this paper introduces a new unsupervised model called MMD Fence GAN. An improved loss function is incorporated into the generator to enhance anomaly detection accuracy. In the loss function of the generator, there are three main components: the encirclement loss limits the generated samples to lie at the decision boundary of the normal samples. The central dispersion loss maximizes the decision boundary's range. By measuring the maximum mean discrepancy between the generated samples and the given samples, the sample dispersion loss avoids issues like model collapse and outliers. Two datasets validate the effectiveness of the model: On the 2D synthetic dataset, it converges faster and pinpoints decision boundaries more precisely than Fence GAN. Both F1-score and recall improve over traditional and advanced deep learning methods on the KDD99 dataset.