Deep Learning of Behaviors for Security

Deep learning has generated much research and commercialization interest recently. In a way, it is the third incarnation of neural networks as pattern classifiers, using insightful algorithms and architectures that act as unsupervised auto-encoders which learn hierarchies of features in a dataset. After a short review of that work, we will discuss computational approaches for deep learning of behaviors as opposed to just static patterns. Our approach is based on structured non-negative matrix factorizations of matrices that encode observation frequencies of behaviors. Example security applications and covert channel detection and coding will be presented.