Generative Adversarial Network used in the field of Network Intrusion Detection has become very common, but mode collapse of Generative Adversarial Network and unbalanced distribution of training dataset in Network Intrusion Detection are problems worth solving. Generator and discriminator of Generative Adversarial Network can not fully learn feature information. In this paper, the Twin Auxiliary Classifier GAN is combined with the idea of mutual information modeling. The training is carried out on the Network Intrusion Detection dataset UNSW-NB15. After comparing the original dataset trained by Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine and Multi-layer Perceptron Machine with the expanded dataset generated by Twin Auxiliary Classifier GAN training, the results show that the methods proposed in this paper can improve the performance of each classifier on the test set.
Recently there has been some interest in using infrared cameras for human detection because of the sharply decreasing prices of infrared cameras. The training data used in our work for developing the probabilistic template consists images known to contain humans in different poses and orientation but having the same height. Multiresolution templates are performed. They are based on contour and edges. This is done so that the model does not learn the intensity variations among the background pixels and intensity variations among the foreground pixels. Each template at every level is then translated so that the centroid of the non-zero pixels matches the geometrical center of the image. After this normalization step, for each pixel of the template, the probability of it being
pedestrian is calculated based on the how frequently it appears as 1 in the training data. We also use periodicity gait to verify the pedestrian in a Bayesian manner for the whole blob in a probabilistic way. The videos had quite a lot of variations in the scenes, sizes of people, amount of occlusions and clutter in the backgrounds as is clearly evident. Preliminary experiments show the robustness.
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