Learning from failure: Trend analysis for compromised accounts and passwords

电脑账户被泄露的原因有：一些人使用了弱密码；一些人创造了远高于正常水平的数字枯竭；有些人处于高风险职位（例如，高管，会计），并且暴露在太多的攻击中。从账户泄露和密码使用中吸取教训将有助于人们设计出更好的防御技术。遵循这一原则，这个Engage项目将开发一套新的算法，这些算法可以自动了解被盗账户和密码使用的趋势，并更早地识别潜在威胁。该项目计划回答以下与计算机安全相关的问题：如何以及在哪里从被泄露帐户的公开数据中检索相关信息？大多数密码有多不安全？用蛮力破解/猜出它们需要多长时间？使用的密码是否遵循任何最佳实践（例如字母+数字）？密码安全性与用户的专业职位（如主管、经理和会计）是否相关？谁的账户最有可能被泄露，他们在哪个行业？被入侵的账户是否比正常的数字耗尽率更高？有了从趋势分析中学到的模式，我们能否建立一个模型来预测哪些账户可能被泄露？该模型是否还能就如何修补可能被泄露的账户的安全问题提出建议？本项目的输出包括：(1)一个数据收集模块，该模块可以从暗网中被泄露账户的公开数据中检索和存储信息；(2)一套算法，可以在趋势分析中回答上述问题；(3)一个预测模型，可以预测用户被承诺的可能性。

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.