Adversarial Data Analytics for National Security

这项研究的目标是设计用于对抗性数据分析的算法技术和系统，检测大型数据集中不良行为的痕迹。这些可以用来保护加拿大在执法，反情报，反恐和金融执法等领域。这些技术和系统也可以应用于主流商业环境，例如客户关系管理，只要那些建立模型的人和那些被建模的人有不同的兴趣。任何描述人类活动的数据集都涉及复杂的非线性现象，尤其是当一些人试图隐藏他们的活动并误导分析时。来自语言和社会行为的数据特别有用，因为在这些领域隐藏和操纵是困难的。该提案的第一个主要目标是更深入地了解如何对语言进行逆向工程以理解其背后的精神状态，以及社交网络中的本地连接如何揭示其成员的力量和意图。一种新的分析方法，即深度学习，在一些长期存在的难题领域取得了成功;第二个主要目标是在对抗领域利用深度学习的思想和技术。这项研究将为各级学生提供政府和行业迫切需要的技能，以保护加拿大免受其他国家，国际犯罪组织和国内犯罪的侵害。

The goal of this research is to design algorithmic techniques and systems for adversarial data analytics, detecting the traces of bad actions in large datasets. These can be used to protect Canada in areas such as law enforcement, counterintelligence, counterterrorism, and financial enforcement. These techniques and systems also have applications in mainstream business settings, such as customer relationship management, whenever those building models and those being modelled have divergent interests. Any dataset that describes the activities of humans involves complex, non-linear phenomena, and this is especially the case when some of the humans are actively trying to conceal their activities and mislead the analysis. Data that are derived from language and social behaviour are especially useful because concealment and manipulation are difficult in these domains.**The first major objective of this proposal is a deeper understanding of how to reverse engineer language to understand the mental state behind it, and how local connections in social networks reveal the power and intent of its members. A new approach to analytics, known as deep learning, has been successful in some longstanding difficult problem domains; the second major objective is to leverage deep learning ideas and techniques in the adversarial domain.**This research will provide students at all levels with skills that are desperately needed in government and industry to defend Canada against other nations, international criminal organisations, and domestic crime.