Exploring the Transition of Research-Derived Cyber-Threat Data

探索研究衍生的网络威胁数据的转变

• 批准号: 1640386
• 负责人: Phillip Porras
• 金额: $ 62.97万
• 依托单位: SRI International
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1640386&HistoricalAwards=false
• 关键词: Exploring; Transition; Research; Derived; Cyber; Exploring; Transition; Research; Derived; Cyber

Transitioning later stage cybersecurity research into operational capabilities remains a national priority. Both the 2011 and 2016 Federal Cybersecurity Research and Development Strategic Plans, collaboratively authored by agencies which participate in the NITRD (Networking and Information Technology R&D) program, explicitly identify Transition to Practice (TTP) as a goal set by the Federal agencies that fund Cybersecurity research, including NSF. The 2016 Plan states that agencies should: "Assess barriers and identify incentives that could accelerate the transition of evidence-validated effective and efficient cybersecurity research results into adopted technologies, especially for emerging technologies and threats." This project considers the unique challenges in the transition of large-scale data or analytic results that are embodied as data feeds, distilled threat intelligence, or online portal services. With the growing attention by U.S. agencies in sponsoring cross-disciplinary research from the Data Science and cybersecurity research communities, the study is both timely and important.Novel security applications can be derived through the use of many newly emerging massively scalable machine learning technologies. Organized cyber-adversaries regularly mount sophisticated large-scale attacks and malware-driven campaigns that target large Universities, research organizations, industries, nations, and even the Internet itself. The dire need for deep reasoning cyber-threat-relevant applications now coincides with the growing availability of massive Internet datasets and the data mining solutions to capitalize on this data. This proposal explores the issues involved in transitioning the results of such large-scale data analytics into practical use.

过渡后期的网络安全研究对运营能力仍然是国家的优先事项。 2011年和2016年，由参与NITRD（网络和信息技术R＆D）计划的机构合作撰写的2011年和2016年联邦网络安全研究与发展战略计划，明确地将过渡到实践（TTP）是由包括NSF在内的基金Cyber​​security Research（包括NSF）的联邦机构设定的目标。 2016年的计划指出，机构应：“评估障碍并确定激励措施，这些激励措施可以加速循证验证有效，有效的网络安全研究结果对采用技术的结果，尤其是对于新兴技术和威胁。”该项目考虑了大规模数据或分析结果的独特挑战，这些挑战被体现为数据提要，蒸馏威胁智能或在线门户服务。随着美国机构在赞助数据科学和网络安全研究社区赞助跨学科研究的越来越关注，该研究既及时又重要。可以通过使用许多新兴新兴的大规模可扩展机器学习技术来得出Novel Security应用程序。有组织的网络对抗者定期进行复杂的大规模攻击和恶意软件驱动的活动，以针对大型大学，研究组织，行业，国家，甚至互联网本身。现在，对网络威胁性的深度推理应用程序的迫切需要与大量互联网数据集的可用性和数据挖掘解决方案的日益增长的相吻合，以利用这些数据。该提案探讨了将这种大规模数据分析结果转变为实际使用的结果所涉及的问题。