  Network attacks have become a major security concern for organizations
worldwide and have also drawn attention in the academics. Recently, researchers
have applied neural networks to detect network attacks with network logs.
However, public network data sets have major drawbacks such as limited data
sample variations and unbalanced data with respect to malicious and benign
samples. In this paper, we present a new approach, protocol fuzzing, to
automatically generate high-quality network data, on which deep learning models
can be trained. Our findings show that fuzzing generates data samples that
cover real-world data and deep learning models trained with fuzzed data can
successfully detect real network attacks.
