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5. CONTRIBUTIONS AND LESSONS LEARNED 5.1 Contributions The following table presents the main contributions provided by this thesis. Objective/Area Contributions Intrusion detection technique trained in a supervised manner thanks to the use of class labels during training. -First application, as far as we know, of a conditional VAE to perform intrusion detection. - ID-CVAE integrates the intrusion label in the decoder layer, which results in a less complex model than an equivalent model that exclusively uses a VAE and with a better detection performance. - For ID-CVAE, the classification process only requires one single training stage followed by as many test stages as distinct values we try to predict. A VAE would require as many training and test stages as there are distinct values label values. Considering that the training phase is the most costly, we can see the improvement in performance obtained by using a conditional VAE (CVAE) - With ID-CVAE for classification we obtain an accuracy over 80% for the NSL-KDD 5 labels scenario, which is better than the values obtained from Random Forest, Linear SVM, Logistic Regression and MLP - Proposal of a new model (ID-CVAE), which is essentially an unsupervised Type of traffic classification - The natural domain for a CNN, which is image processing, is expanded to Network Traffic Classification (NTC) in an easy and natural way. It demonstrates that a CNN can be successfully applied to NTC classification, giving an easy way to extend the image-processing paradigm of CNN to a vector time-series data (in a similar way to previous extensions to text and audio processing). - First application, as far as we know, of a CNN+RNN model to an NTC problem. - It is shown that a RNN combined with a CNN provides better detection results than alternative algorithms without requiring any feature engineering, usual when applying other models. - A robust model that gives excellent F1 detection scores under a highly unbalanced dataset, with over 100 different classification labels is provided. It works with a very small number of features and does not require feature engineering. The model is trained with high-level header-based data extracted from the packets. It is not required to rely on IP addresses or payload data, which are probably confidential or encrypted. Traffic prediction -Results of applying machine learning techniques to forecast the on-off activity state of IoT mobile devices, using time-series and no-time-series methods, are presented. Data from real IoT mobile devices is employed. It provides new insights comparing the results for time-series and non-time- series methods, applying the methods to a large number of devices with very different connectivity behaviours. - Data pre-processing to present the data in a form that could be used by both time-series and non-time-series methods. - Test results were achieved with a specifically developed cross-validation process, applied to both, time-series and non-time-series methods. - Novel results obtained from the application of random forest. - Previous works on this subject have focused on the prediction of data volume transmitted (which is a continuous variable), however this paper focuses on Doctoral Thesis: Novel applications of Machine Learning to NTAP - 56PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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