Adversarial Data Analytics for National Security

本研究的目标是设计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。这些可以用来在执法、反情报、反恐和金融执法等领域保护加拿大。这些技术和系统在主流商业环境中也有应用，比如客户关系管理，只要那些构建模型的和那些被建模的有不同的兴趣。任何描述人类活动的数据集都涉及复杂的非线性现象，特别是当一些人类积极地试图掩盖他们的活动并误导分析时。来自语言和社会行为的数据尤其有用，因为在这些领域隐藏和操纵是困难的。

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.