  While applications of machine learning in cyber-security have grown rapidly,
most models use manually constructed features. This manual approach is
error-prone and requires domain expertise. In this paper, we design a
self-supervised sequence-to-sequence model with attention to learn an embedding
for data routinely used in cyber-security applications. The method is validated
on two real world public data sets. The learned features are used in an anomaly
detection model and perform better than learned features from baseline methods.
