  Software flaw detection using multimodal deep learning models has been
demonstrated as a very competitive approach on benchmark problems. In this
work, we demonstrate that even better performance can be achieved using neural
architecture search (NAS) combined with multimodal learning models. We adapt a
NAS framework aimed at investigating image classification to the problem of
software flaw detection and demonstrate improved results on the Juliet Test
Suite, a popular benchmarking data set for measuring performance of machine
learning models in this problem domain.

Machine Learning;Artificial Intelligence;Cryptography and Security;Machine Learning