CAREER: Developing Robust Longitudinal Indicators and Early Warnings of Cybercrime

职业：开发稳健的纵向指标和网络犯罪预警

• 批准号: 1652610
• 负责人: Tyler Moore
• 金额: $ 42.1万
• 依托单位: University of Tulsa
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1652610&HistoricalAwards=false
• 关键词: CAREER; Developing; Robust; Longitudinal; Indicators

The proliferation of cybercrime has created an opportunity for researchers to study cybersecurity using a data-driven, and ultimately scientific, approach. This project seeks to improve the process of cybercrime data collection and analysis for the purpose of reducing its harm. Effort-based indicators, which measure the impact of defender actions such as the time taken to clean a compromised resource, are being constructed for use in analyzing data gathered on multiple categories of cybercrime. Indicators are being collected throughout the duration of the project in order to enable longitudinal analysis, for example, to measure any improvements that result from dissemination. It is also being investigated whether it is feasible to devise and deploy a prototype early-warning system that proactively alerts defenders quickly enough to arrest concentrated spikes in cybercriminal activity. The project's educational objective is to advance the science of cybersecurity by contributing public datasets of cybercriminal activity to be shared with other researchers and incorporated into curriculum modules. Instructional initiatives include improving existing curricula and publishing stand-alone resources that can be adopted by others.The project is advancing understanding of how to construct reliable cybercrime indicators. Effort-based indicators help mitigate information asymmetries, so that anyone can see which defenders are working harder to clean up compromised resources. This could in turn improve the response to cybercrime overall by solving coordination problems in which individual defenders shirk responsibility. The project is also advancing understanding of how the response to cybercrime changes over time by collecting data for several years. If widely shared, the indicators can help incentivize defenders to fight cybercrime over the long term. The project seeks to identify not only how to construct the indicators, but also what is the most effective way to share them. The early-warning system, if successful, could greatly enhance the efficiency of countermeasures by helping defenders get ahead of the shifting tactics used by attackers. In the education program supported by the project, datasets are being added to curriculum modules that teach scientific approaches to cybersecurity. Modules are being designed for not only graduate and undergraduate degree programs, but also for middle and high school students, free online courses co-developed by the PI, and a security economics textbook under development.

网络犯罪的扩散为研究人员创造了使用数据驱动的，最终是科学的方法来研究网络安全的机会。该项目旨在改善网络犯罪数据收集和分析的过程，以减少其危害。基于努力的指标，衡量了辩护人行动的影响，例如清洁折磨资源所花费的时间，用于分析在多种类别的网络犯罪上收集的数据。在整个项目期间，正在收集指标，以使纵向分析能够衡量通过传播导致的任何改进。还正在调查是否可以设计和部署原型的早期制作系统是否可行，该系统主动地积极提醒防御者，足以阻止浓缩的网络犯罪活动。该项目的教育目标是通过贡献网络犯罪活动的公共数据集来推进网络安全科学，并与其他研究人员共享并纳入课程模块中。教学计划包括改善其他人可以采用的现有课程和发布独立资源。该项目正在促进对如何构建可靠的网络犯罪指标的理解。基于努力的指标有助于减轻信息不对称，以便任何人都可以看到哪些捍卫者正在努力工作以清理妥协的资源。反过来，这可以通过解决各个捍卫者逃避责任的协调问题来改善整个网络犯罪的反应。该项目还在促进对网络犯罪反应如何通过收集数据数年来变化的理解。如果广泛分享，这些指标可以帮助捍卫者长期与网络犯罪作战。该项目不仅试图确定如何构建指标，而且还要确定分享它们的最有效方法。如果成功的话，早期的制度系统可以通过帮助防守者领先攻击者使用的转移策略，从而大大提高对策的效率。在该项目支持的教育计划中，数据集被添加到教授科学方法网络安全方法的课程模块中。模块不仅是为研究生和本科学位课程设计的，而且还为中学生，免费的在线课程，由PI共同开发，以及正在开发的安全经济学教科书。

项目成果

Ten years of attacks on companies using visual impersonation of domain names

使用域名视觉模仿对公司进行的十年攻击

• 发表时间: 2020
• 期刊: Proceedings of the APWG Symposium on Electronic Crime Research
• 作者: Simpson, Geoffrey;Moore, Tyler;Clayton, Richard
• 通讯作者: Clayton, Richard

Empirical analysis of losses from business-email compromise

商业电子邮件泄露造成的损失的实证分析

• 发表时间: 2020
• 期刊: Proceedings of the APWG Symposium on Electronic Crime Research
• 作者: Simpson, Geoffrey;Moore, Tyler
• 通讯作者: Moore, Tyler

The County Fair Cyber Loss Distribution: Drawing Inferences from Insurance Prices

县公平网络损失分布：从保险价格中推断

• DOI: 10.1145/3434403
• 发表时间: 2021
• 期刊: Digital Threats: Research and Practice
• 作者: Woods, Daniel W.;Moore, Tyler;Simpson, Andrew C.
• 通讯作者: Simpson, Andrew C.