Abstract: Systems, devices, and methods for training a machine learning model and classifying encoded data include an exemplary method that includes: receiving first training data, by a variational autoencoder (VAE) comprising a probabilistic encoder and a probabilistic decoder; and encoding the first training data, by the probabilistic encoder, to generate encoded data in an embedding space; decoding the encoded data, by the probabilistic decoder, to generate decoded data; computing a loss function, comprising a hinge-loss term, based on the decoded data and the encoded data; and adjusting one or more parameters of the VAE based on the computed loss function including the hinge-loss term. The exemplary method may include classifying, by a linear support vector machine (SVM), the encoded data in the embedding space wherein classifying comprises determining the hyperplane in the embedding space separating the first class of the encoded data from the second class of the encoded data.

Abstract: A system for refining one or more synthetic network traffic models is provided. The system generates synthetic network traffic data by applying a deterministic generative rule set to generate at least part of the synthetic network traffic data and applying a probabilistic generative model to generate at least part of the synthetic network traffic data. The system generates an assessment of the generated synthetic network traffic data by applying a deterministic discriminative rule set to the synthetic network traffic data and by applying a discriminative classifier model to the synthetic network traffic data. The system updates one or both of the probabilistic generative model and the discriminative classifier model based on the generated assessment.

Abstract: A method for analyzing data is provided. The method includes generating, using a processing device, a graph from raw data, the graph including a plurality of nodes and edges, deriving, using the processing device, at least one label for each node using a deep belief network, and identifying, using the processing device, a predetermined pattern in the graph based at least in part on the labeled nodes.