Measurable and Deployable Security: Gaps, Successes, and Opportunities

D. Yao
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Abstract

Security measurement helps identify deployment gaps and present extremely valuable research opportunities. However, such research is often deemed as not novelty by academia. I will first share my research journey designing and producing a high-precision tool CryptoGuard for scanning cryptographic vulnerabilities in large Java projects. That work led us to publish two benchmarks used for systematically assessing state-of-the-art academic and commercial solutions, as well as help Oracle Labs integrate our detection in their routine scanning. Other specific measurement and deployment cases to discuss include the Payment Card Industry Data Security Standard, which was involved in high-profile data breach incidents, and fine-grained Address Space Layout Randomization (ASLR). The talk will also point out the need for measurement in AI development in the context of code repair. Broadening research styles by accepting and encouraging deployment-related work will facilitate our field to progress towards maturity.
可度量和可部署的安全性:差距、成功和机会
安全度量有助于确定部署差距,并提供极有价值的研究机会。然而,学术界往往认为这类研究并不新颖。我将首先分享我的研究历程,我设计并制作了一个高精度工具CryptoGuard,用于扫描大型Java项目中的加密漏洞。这项工作使我们发布了两个基准,用于系统地评估最先进的学术和商业解决方案,并帮助Oracle实验室将我们的检测集成到他们的日常扫描中。要讨论的其他具体测量和部署案例包括支付卡行业数据安全标准(涉及备受瞩目的数据泄露事件)和细粒度地址空间布局随机化(ASLR)。该演讲还将指出,在代码修复的背景下,人工智能开发中需要测量。通过接受和鼓励与部署相关的工作来拓宽研究风格,将促进我们的领域走向成熟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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