论文标题
使用神经体系结构搜索改进多模式深度学习模型中的软件漏洞检测
Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models
论文作者
论文摘要
使用多模式深度学习模型的软件缺陷检测已被证明是基准问题的一种非常有竞争力的方法。在这项工作中，我们证明，使用神经体系结构搜索（NAS）与多模式学习模型相结合，可以实现更好的性能。我们调整了旨在研究图像分类的NAS框架，以解决软件漏洞检测问题，并在朱丽叶测试套件上展示了改进的结果，Juliet Test Suite是一个流行的基准测试数据集，用于测量此问题域中机器学习模型的性能。
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.
