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5.2 Lessons learned The following table presents the lessons learned from the research carried out for this thesis: Objective/Area Lessons learned Intrusion detection providing better results than classic supervised ML methods: random forest, SVM, MLP and logistic regression. - Very simple encoder and decoder networks (3 layers only) are enough to obtain best results. Increasing the number of layers does not improve results. - Using a conditional VAE instead of a VAE provides many advantages in terms of a faster classification algorithm. -VAEs and conditional VAES present robust and easier training than alternatives that do not use variational methods. -An unsupervised algorithm (VAE) can be used for intrusion detection Type of traffic classification - In a prediction/detection problem related to time-series of vectors, in addition to using an RNN which is the natural choice given the time-series nature of the problem, it is a good strategy to include a CNN as an initial step in a deep learning architecture. A CNN, in addition to performing feature engineering, is able to extract time patterns that are useful when they are subsequently processed by the RNN. - When a sequence of packet headers is used to predict the type of traffic of a network flow, it is not necessary to deal with the entire sequence; a small number of packets are enough to make the prediction with high accuracy. Traffic prediction - One week of historical data is enough to provide good forecasts - Independently of the method, the on-off connectivity from IoT devices presents a rich periodic structure, allowing good prediction results, even with short training data. - The non-time-series methods require, in general, less training time than the time-series-methods. - From the results obtained, we expect in future works to find additional predictors (covariates) that will probably improve the predicting power of the methods presented. One of these new predictors could be obtained by performing clustering of the signals, trying to use the cluster index as an additional new predictor. The main problem will be the high computational demand for this task. - It is interesting that logistic regression which is a very simple model can provide an accuracy comparable (in practical terms) to more sophisticated models (e.g. ARIMAX, Random Forest, ARIMA,..). QoE estimation - It is possible to apply new deep learning models whose origin focused mainly on the areas of video, audio and language processing for the prediction of QoE of transmitted videos. - We extended the study with the inclusion of a GP Classifier that, being a non- parametric model, could make full use of the scarce data available. This inclusion provides a slight improvement in the prediction results. In addition, it requires much more memory and processing time, which makes it less useful than expected. - The performance does not increase when increasing the number of samples per flow beyond three, implying that prediction is conditioned on the amount of previous information, but information too distant in time is not only less useful but indeed a problem. Similarly, reducing the number of samples below three also decreases the prediction performance. - From this experience, we plan to investigate the application of generative models (e.g. variational autoencoders) to create synthetic data and explore Doctoral Thesis: Novel applications of Machine Learning to NTAP - 59PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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