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decoder does not improve the results, making the training more difficult. It is important to provide a fair number of epochs for training the models, usually 50 or higher. We have used Tensorflow to implement all the ID-CVAE models, and the python package scikit-learn [27] to implement the different classifiers. All computations have been performed on a commercial PC (i7-4720-HQ, 16 GB RAM). 5. Conclusions and Future Work This work is unique in presenting the first application of a conditional VAE (CVAE) to perform classification on intrusion detection data. More important, the model is also able to perform feature reconstruction, for which there is no previous published work. Both capabilities can be used in current NIDS, which are part of network monitoring systems, and particularly in IoT networks [1]. We have demonstrated that the model performs extremely well for both tasks, being able for example to provide better classification results on the NSL-KDD Test dataset than well- known algorithms: random forest, linear SVM, multinomial logistic regression and multi-layer perceptron. The model is also less complex than other classifier implementations based on a pure VAE. The model operates creating a single model in a single training step, using all training data irrespective of their associated labels. While a classifier based on a VAE needs to create as many models as there are distinct label values, each model requiring a specific training step (one vs. rest). Training steps are highly demanding in computational time and resources. Therefore, reducing its number from n (number of labels) to 1 is an important improvement. When doing feature reconstruction, the model is able to recover missing categorical features with three, 11 and 70 values, with an accuracy of 99%, 92%, and 71%, respectively. The reconstructed features are generated from a latent multivariate probability distribution whose parameters are learned as part of the training process. This inferred latent probability distribution serves as a proxy for obtaining the real probability distribution of the features. This inference process provides a solid foundation for synthesizing features as similar as possible to the originals. Moreover, by adding the sample labels, as an additional input to the decoder, we improve the overall performance of the model making its training easier and more flexible. Extensive performance metrics are provided for multilabel classification and feature reconstruction problems. In particular, we provide aggregated and one vs. rest metrics for the predicted/reconstructed labels, including accuracy, F1 score, precision and recall metrics. Finally, we have presented a detailed description of the model architecture and the operational steps needed to perform classification and feature reconstruction. Considering future work, after corroborating the good performance of the conditional VAE model, we plan to investigate alternative variants as ladder VAE [28] and structured VAE [29], to explore their ability to learn the probability distribution of NIDS features. Acknowledgments: This work has been partially funded by the Ministerio de Economía y Competitividad del Gobierno de España and the Fondo de Desarrollo Regional (FEDER) within the project “Inteligencia distribuida para el control y adaptación de redes dinámicas definidas por software, Ref: TIN2014-57991-C3-2-P”, and the Project “Distribucion inteligente de servicios multimedia utilizando redes cognitivas adaptativas definidas por software”, Ref: TIN2014-57991-C3-1-P, in the Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia, Subprograma Estatal de Generación de Conocimiento. Author Contributions: Manuel Lopez Martín conceived and designed the original models and experiments. Belen Carro and Antonio Sanchez-Esguevillas have supervised the work, guided the experiments and critically reviewed the paper to produce the manuscript. Jaime Lloret Doctoral Thesis: Novel applications of Machine Learning to NTAP - 144PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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