Towards Detection of Unknown Polymorphic Patterns Using Prior Knowledge

Abstract

The presented paper proposes a novel approach for detecting unknown polymorphic patterns in sequences composed of random symbols and of known polymorphic patterns. We propose to represent rules that drive pattern generation as regular expressions. To detect unknown patterns, we first incorporate knowledge on known rules into a Convolutional Autoencoder (CAE), then we train the CAE with additional objective to prevent weights from learning the already known patterns. Analysis of training results provides statistically significant information on presence or absence of polymorphic patterns that were not previously known.