Adversarial Data Analytics for National Security

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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.