  Signature and anomaly based techniques are the quintessential approaches to
malware detection. However, these techniques have become increasingly
ineffective as malware has become more sophisticated and complex. Researchers
have therefore turned to deep learning to construct better performing model. In
this paper, we create four different long-short term memory (LSTM) based models
and train each to classify malware samples from 20 families. Our features
consist of opcodes extracted from malware executables. We employ techniques
used in natural language processing (NLP), including word embedding and
bidirection LSTMs (biLSTM), and we also use convolutional neural networks
(CNN). We find that a model consisting of word embedding, biLSTMs, and CNN
layers performs best in our malware classification experiments.
