Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models

Autor:	Cooper, Alexis, Zhou, Xin, Heidbrink, Scott, Dunlavy, Daniel M.
Rok vydání:	2020
Předmět:	Statistics - Machine Learning Computer Science - Artificial Intelligence Computer Science - Cryptography and Security Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 10 pages, 5 figures, 4 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2009.10644 Zobrazit plný text záznamu View this record from Arxiv