CAREER: At-scale Analysis of Issues in Cyber-Security and Software Engineering

职业：网络安全和软件工程问题的大规模分析

• 批准号: 1552836
• 负责人: Christopher White
• 金额: $ 48.72万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-15 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1552836&HistoricalAwards=false
• 关键词: CAREER; scale; Analysis; Issues; Cyber

One of the most significant challenges in cybersecurity is that humans are involved in software engineering and inevitably make security mistakes in their implementation of specifications, leading to software vulnerabilities. A challenge to eliminating these mistakes is the relative lack of empirical evidence regarding what secure coding practices (e.g., secure defaults, validating client data, etc.), threat modeling, and educational solutions are effective in reducing the number of application-level vulnerabilities that software engineers produce. This research aims to perform experiments analyzing programming assignment submissions to Massively Open Online Courses (MOOCs) before and after secure coding and threat modeling techniques are taught to empirically measure their impact on the rate of security vulnerabilities in assignment implementations. A key component of this research will be the use of MOOC assignment specifications and variations that have the potential to be affected by common cybersecurity vulnerabilities, such as problems with input validation to web applications or privilege escalation on mobile platforms. Because these critical security implementation issues will be known ahead of time, the MOOC assignments will allow automated assessment of how successfully each assignment implementation manages these security issues.Key questions investigated by this research include analyzing the impact of varying secure coding and threat modeling techniques on vulnerability production in software, what level of abstraction these techniques need to be taught at to be effective, the relative return on investment of threat modeling vs. automated vulnerability assessment effort, and the comparative effectiveness of making developers aware of security issues versus requiring active application of secure coding and threat modeling techniques. The broader impact of this research is substantial. Very little empirical data is available for organizations to use to properly value the secure coding and threat modeling techniques that have been developed. By creating a large body of rigorous evidence to illustrate how effective (or possibly not effective) different techniques are, the research will allow organizations to evaluate their return on investment and improve the use of these techniques in the software engineering process.

网络安全方面最重要的挑战之一是，人类参与软件工程，不可避免地犯了安全错误，从而导致软件漏洞。消除这些错误的挑战是，相对缺乏有关哪种安全编码实践（例如，安全默认，验证客户数据等），威胁建模和教育解决方案有效减少应用程序级别漏洞的数量，这些证据相对缺乏经验证据。软件工程师生产。这项研究旨在进行实验，以分析编程作业提交内容，以在安全的编码和威胁建模技术之前和之后大规模打开在线课程（MOOC），以经验来衡量其对分配实施中安全脆弱性率的影响。这项研究的关键组成部分是使用MOOC分配规格和有可能受到常见网络安全漏洞影响的变化，例如对Web应用程序的输入验证问题或移动平台上的特权升级。由于这些关键的安全实施问题将提前知道，因此MOOC任务将允许自动评估每个任务实施如何管理这些安全问题。本研究所调查的关键问题包括分析不同的安全编码和威胁建模技术对上的影响软件中的脆弱性生产，需要教授这些技术的何种抽象，要有效，威胁建模与自动漏洞评估工作的相对投资回报率以及使开发人员意识到安全问题而不是需要主动应用程序的比较有效性安全的编码和威胁建模技术。这项研究的更广泛影响是巨大的。几乎没有经验数据可供组织使用，以正确地重视已开发的安全编码和威胁建模技术。通过创建大量严格的证据来说明不同技术的有效性（或可能无效），该研究将使组织能够评估其投资回报率并改善这些技术在软件工程过程中的使用。