一句话：本文提出用RelGAN自动合成安全需求规格，并在法院案件管理系统案例中验证其可行性与专家认可度。
安全需求工程通常依赖人工，容易出错，且因网络安全人员与需求工程师之间的知识差距而被忽视。本文旨在自动化安全需求规格的推荐与合成，帮助需求工程师获取和编写安全需求。作者研究使用关系生成对抗网络（RelGAN）自动合成安全需求规格，并以印第安纳州最高法院州法院管理局开发的法院案件管理系统（CCMS）为真实案例进行评估。结果表明，RelGAN在合成安全需求规格方面具有实用性，并获得领域专家认可；该研究为软件需求合成中的GAN应用提供了有前景的结果、基线以及局限性与后续方向。
Security requirements engineering is a manual and error-prone activity that is often neglected due to the knowledge gap between cybersecurity professionals and software requirements engineers. In this paper, we aim to automate the process of recommending and synthesizing security requirements specifications and therefore supporting requirements engineers in soliciting and specifying security requirements. We investigate the use of Relational Generative Adversarial Networks (GANs) in automatically synthesizing security requirements specifications. We evaluate our approach using a real case study of the Court Case Management System (CCMS) developed for the Indiana Supreme Court's Division of State Court Administration. We present an approach based on RelGAN to generate security requirements specifications for the CCMS. We show that RelGAN is practical for synthesizing security requirements specifications as indicated by subject matter experts. Based on this study, we demonstrate promising results for the use of GANs in the software requirements synthesis domain. We also provide a baseline for synthesizing requirements, highlight limitations and weaknesses of RelGAN and define opportunities for further investigations.
@inproceedings{KoscinskiHM23,
title = {On-Demand Security Requirements Synthesis with Relational Generative Adversarial Networks},
author = {Viktoria Koscinski and Sara Hashemi and Mehdi Mirakhorli},
booktitle = {ICSE},
year = {2023},
doi = {10.1109/icse48619.2023.00139}
}