如何更好地区分安全漏洞报告（使用双超参数优化
How to Better Distinguish Security Bug Reports (using Dual Hyperparameter Optimization
摘要：
背景资料：为了使公众不容易受到黑客的攻击，安全漏洞报告需要由一小群工程师处理，然后才能被广泛讨论。但是，学习如何区分安全错误报告与其他错误报告是具有挑战性的，因为它们可能很少发生。能够找到这种稀缺目标的数据采掘方法需要大量的优化工作。目标：这项研究的目的是帮助从业者，因为他们努力优化方法，试图区分罕见的安全错误报告和其他错误报告。方法：我们提出的方法，称为Swift，是一种双重优化器，可以优化学习器和预处理器选项。由于这是一个很大的选项空间，Swift使用了一种称为epsilon-dominance的技术，该技术可以学习如何避免不会显着提高性能的操作。测试结果：与最近的最先进的结果（来自FARSEC，发表在TSE '18）相比，我们发现Swift对预处理器和学习器的双重优化比单独优化它们更有用。例如，在一项针对Chromium数据集的安全漏洞报告的研究中，FARSEC和Swift的召回率中位数分别为15.7%和77.4%。再举一个例子，在Ambari项目的数据实验中，召回率中位数从21.5%提高到85.7%（FARSEC到SWIFT）。结论：总的来说，我们的方法可以快速优化模型，实现比现有技术更好的召回。这些召回的增加与假阳性率的适度增加（从8%到24%，中位数）相关。对于未来的工作，这些结果表明，双重优化是实用和有用的。
英文摘要：
Background: In order that the general public is not vulnerable to hackers, security bug reports need to be handled by small groups of engineers before being widely discussed. But learning how to distinguish the security bug reports from other bug reports is challenging since they may occur rarely. Data mining methods that can find such scarce targets require extensive optimization effort. Goal: The goal of this research is to aid practitioners as they struggle to optimize methods that try to distinguish between rare security bug reports and other bug reports. Method: Our proposed method, called Swift, is a dual optimizer that optimizes both learner and pre-processor options. Since this is a large space of options, Swift uses a technique called epsilon-dominance that learns how to avoid operations that do not significantly improve performance. Result: When compared to recent state-of-the-art results (from FARSEC which is published in TSE'18), we find that the Swift's dual optimization of both pre-processor and learner is more useful than optimizing each of them individually. For example, in a study of security bug reports from the Chromium dataset, the median recalls of FARSEC and Swift were 15.7% and 77.4%, respectively. For another example, in experiments with data from the Ambari project, the median recalls improved from 21.5% to 85.7% (FARSEC to SWIFT). Conclusion: Overall, our approach can quickly optimize models that achieve better recalls than the prior state-of-the-art. These increases in recall are associated with moderate increases in false positive rates (from 8% to 24%, median). For future work, these results suggest that dual optimization is both practical and useful.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
