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authors of [62] use a stacked autoencoder to detect multi-label attacks in an IEEE 802.11 network. In [63] is implemented an intrusion classifier combining spectral clustering and deep neural networks in an ensemble algorithm. An and Cho [64] presents a classifier solution using a VAE in the intrusion detection field. The following table presents a summary of the main works related to the research carried out for this thesis (anomaly-based NIDS). It provides a reference to the document, the data set used and the scope of the work. Objective/Area Ref. Dataset Scope Intrusion detection [64] KDD99 -Classifier solution using a VAE in the intrusion detection field, but it is a VAE (not CVAE) with a different architecture to the one presented in this thesis. [65] Yahoo S5 time- series dataset - They use a recurrent neural network (RNN) with a CVAE to perform anomaly detection on a big time- series dataset. It is applied to generic multivariate time-series. The architecture is different to the one presented in this thesis, and the results are not related to NIDS. [66] Multivariate time- series from robot movements -It is employed an RNN with a VAE to perform anomaly detection on multivariate time-series coming from a robot. Data and results are not applicable to NIDS. [24] MNIST -The authors apply a CVAE to a semi-supervised image classification problem. Data and results are not applicable to NIDS. [60] Simulated IoT network - A neural network is used for detecting DoS attacks in a simulated IoT network, reporting an accuracy of 99.4%. [61] In-vehicle Controller Area Network (CAN) -This work presents a classifier which detects intrusions in an in-vehicle Controller Area Network (CAN), using a deep neural network (DNN) pre- trained with a Deep Belief Network (DBN). the DNN is trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. [62] AWID dataset focused on intrusion detection - The authors of this work use a stacked autoencoder to detect multilabel (4-class) attacks in an IEEE 802.11 network with an overall accuracy of 98.6%. They use a sequence of sparse auto-encoders but they do not use variational autoencoders. [63] NSL-KDD - They implement an intrusion classifier combining spectral clustering and deep neural networks in an ensemble algorithm. They used the NSL-KDD dataset in different configurations, reporting an overall accuracy of 72.64% for a similar NSL-KDD configuration to the one presented in this paper. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 26PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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