Adversarial knowledge discovery

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Adversarial knowledge discovery builds models from data in settings where the interests of those doing the modelling are not aligned with the interests of some of those being modelled. This includes law enforcement, counterterrorism, border control, fraud, anti-money-laundering and, increasingly, mainstream domains such as customer relationship management. Criminal activity costs Canada perhaps as much as 6% of GDP, billions of dollars per year; and some crime and terrorism also has costs in property damage, injuries, and lives lost. At present, the process of understanding the actions of adversaries from the traces they leave in data is driven by analysts who interrogate the data for patterns that they think may be significant. This is laborious and requires analysts to be skilled, experienced, and creative. Their results can be improved substantially by adding an inductive component -- constructing plausible models algorithmically from the data, and asking analysts to assess them. This is commonplace in mainstream knowledge discovery, but more difficult in adversarial settings because likely patterns are harder to discover: because adversaries are actively trying to vary their activities, because they are trying to conceal themselves from analysis, and because they may be trying to manipulate the analysis process. The next stage of my work in this area tackles four problems: (1) finding algorithms to rank data records without assumptions, based on 'interestingness' so that analyst attention can be focused on the critical few; (2) extracting information from text in a deeper way,taking ideas about how we, as humans, generate language and making them algorithmic; (3) extracting richer information from graph-structured data (for example, relational connections among people based on their interactions; and (4) algorithmically discovering potential meanings of clusters and other structures in data. Progress in solving these problems will make Canada safer, will reduce the risks to our population from criminal and terrorist violence, and reduce the substantial economic cost of crime and fraud.