Adversarial knowledge discovery

对抗性知识发现

• 批准号: 5532-2011
• 负责人: Skillicorn, David
• 金额: $ 2.11万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=568405
• 关键词: Adversarial; knowledge; discovery

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.

对抗性知识发现从数据中构建模型，在这种情况下，建模者的利益与一些被建模者的利益不一致。这包括执法、反恐、边境管制、欺诈、反洗钱以及越来越多的主流领域，如客户关系管理。犯罪活动每年给加拿大造成的损失可能高达国内生产总值的6%，数十亿美元;一些犯罪和恐怖主义也造成财产损失、伤害和生命损失。 目前，从对手在数据中留下的痕迹中理解对手行动的过程是由分析师驱动的，他们询问数据，寻找他们认为可能重要的模式。这是费力的，需要分析师熟练，有经验和创造性。他们的结果可以通过增加一个归纳成分来大大改善-从数据中通过算法构建合理的模型，并要求分析师评估它们。 这在主流知识发现中很常见，但在对抗性环境中更困难，因为可能的模式更难发现：因为对手正在积极地尝试改变他们的活动，因为他们试图隐藏自己的分析，因为他们可能试图操纵分析过程。我在这一领域的下一阶段工作要解决四个问题：（1）找到算法来对数据记录进行排名，而不需要假设，基于“兴趣度”，这样分析师的注意力就可以集中在关键的少数人身上;（2）以更深的方式从文本中提取信息，了解我们作为人类如何生成语言，并使它们成为算法;（3）从图形结构数据中提取更丰富的信息（例如，基于交互的人与人之间的关系连接）;（4）通过算法发现数据中聚类和其他结构的潜在含义。 在解决这些问题方面取得进展将使加拿大更加安全，将减少犯罪和恐怖主义暴力对我国人民的风险，并减少犯罪和欺诈的巨大经济成本。