Learning from failure: Trend analysis for compromised accounts and passwords

从失败中学习：帐户和密码泄露的趋势分析

• 批准号: 516553-2017
• 负责人: Wu, Kui
• 金额: $ 1.82万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640427
• 关键词: Learning; failure; Trend; analysis; compromised

Computer accounts get compromised for reasons: some used weak passwords; some created a digital exhaustmuch higher than normal; some were at high-risk positions (e.g., executives, accounting) and were exposed totoo many attacks. Learning lessons from compromised accounts and password use would help people designbetter defense technologies. Following this principle, this Engage project is to develop a set of new algorithms,which automatically learn the trends in compromised accounts and password use and identify potential threatsearlier. The project plans to answer the following questions related to computer security: how and where toretrieve relevant information from publicly available data on compromised accounts? How insecure are mostpasswords? How long would it take to brute force crack/guess them? Do the passwords being used follow anybest practice (e.g., letters + numbers)? Are there correlations with password security and the user's professionalpositions (e.g., executives, managers, and accounting)? Who are the accounts most likely to get compromisedand what industry are they in? Do the compromised accounts have a higher than normal digital exhaustpresence? With the patterns learnt from trend analysis, could we build a model to predict which accounts arelikely to get compromised? Could the model also give recommendations on how to patch the security problemsof accounts that are likely to get compromised?The output of this project includes: (1) a data collection module that can retrieve and store information frompublicly available data on compromised accounts from dark web, (2) a set of algorithms that can answer theabove-raised questions in trend analysis, and (3) a prediction model that can predict a user's likelihood to becompromised.

计算机帐户受到威胁的原因有：一些使用了弱密码；有些人产生的数字消耗远高于正常水平；有些人处于高风险职位（例如高管、会计）并遭受过多的攻击。从被盗帐户和密码使用中吸取教训将有助于人们设计更好的防御技术。遵循这一原则，这个 Engage 项目旨在开发一套新算法，自动学习被盗帐户和密码使用的趋势，并尽早识别潜在威胁。该项目计划回答以下与计算机安全相关的问题：如何以及在哪里从受感染帐户的公开数据中检索相关信息？大多数密码有多不安全？暴力破解/猜测它们需要多长时间？使用的密码是否遵循最佳实践（例如字母+数字）？密码安全性和用户的专业职位（例如主管、经理和会计）是否存在相关性？谁的账户最有可能受到威胁？他们属于什么行业？被盗帐户的数字耗尽程度是否高于正常水平？利用从趋势分析中学到的模式，我们可以建立一个模型来预测哪些帐户可能会受到损害吗？该模型是否还可以就如何修补可能受到威胁的帐户的安全问题提供建议？该项目的输出包括：（1）一个数据收集模块，可以从暗网中受威胁帐户的公开数据中检索和存储信息；（2）一组可以回答趋势分析中上述问题的算法；（3）一个可以预测用户受到威胁的可能性的预测模型。