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classification results obtained from the application of original and synthesized data to several classification algorithms. To conclude the comparative of results we have checked the results obtained with the new model compared to some well-known over-sampling methods: SMORE, SMOTE-Borderline, SMOTE-ENN, SMOTE-Tomek, ADASYN. 7.5.4 Results/Conclusions This work is the response to several challenges: - Generate synthetic data for an intrusion detection dataset, with many and heterogeneous features both continuous and discrete and with a highly imbalanced distribution of intrusion labels. This has been achieved by using a new generative model based on a conditional VAE. - To show that the synthetic generated data have similar probabilistic structure to the original data. Verifying this similarity is a hard problem since it involves comparing the probability distributions of multivariate vectors (116 features) with non-Gaussian marginals (discrete and continuous features) and complex joint probability distributions. The challenge is twofold: obtain the joint probability distributions and compare them. To handle these problems, we have proposed several methods based on extended histograms and the comparison of classification results under different scenarios of training with original and synthetic data - To show that the synthetic data generated with the new architecture produces better results than synthetic data generated by state-of-the-art (SOTA) over-sampling methods. This has been shown when comparing accuracy and F1 classification results when using training data generated by several over-sampling algorithms including the proposed one. We demonstrate that both accuracy and F1 are improved when the new architecture is used. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 78PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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