Efficient Detection of Advanced Persistent Threats

高效检测高级持续威胁

• 批准号: 521296-2017
• 负责人: Adams, Carlisle
• 金额: $ 1.82万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640099
• 关键词: Efficient; Detection; Advanced; Persistent; Threats

The objective of this project is to research and develop novel methods and technologies for the accurateidentification of the presence of Advanced Persistent Threats (APTs), specifically in the case of an attacker thatis using a host computer at the same time as the legitimate user, using the legitimate user's credentials.The project will explore the use of artificial intelligence / machine learning techniques in conjunction withother methods (Bayesian, causal, rule, etc.) to significantly improve early APT detection.

该项目的目标是研究和开发新的方法和技术，用于准确识别高级持续性威胁（APT）的存在，特别是在攻击者同时使用主机作为合法用户的情况下，使用合法用户的凭证。该项目将探索人工智能/机器学习技术与其他方法的结合使用（贝叶斯、因果、规则等）以显著提高早期APT检测。