Reference number : GC-2017-Q3-00457, amendment number : , agreement type : G, recipient legal name : Wu, Kui (University of Victoria), recipient country : CA, recipient province : nan, recipient city : British Columbia|Colombie-Britannique, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Computer accounts get compromised for reasons: some used weak passwords; some created a digital exhaust_x000D_
much higher than normal; some were at high-risk positions (e.g., executives, accounting) and were exposed to_x000D_
too many attacks. Learning lessons from compromised accounts and password use would help people design_x000D_
better defense technologies. Following this principle, this Engage project is to develop a set of new algorithms,_x000D_
which automatically learn the trends in compromised accounts and password use and identify potential threats_x000D_
earlier. The project plans to answer the following questions related to computer security: how and where to_x000D_
retrieve relevant information from publicly available data on compromised accounts? How insecure are most_x000D_
passwords? How long would it take to brute force crack/guess them? Do the passwords being used follow any_x000D_
best practice (e.g., letters + numbers)? Are there correlations with password security and the user's professional_x000D_
positions (e.g., executives, managers, and accounting)? Who are the accounts most likely to get compromised_x000D_
and what industry are they in? Do the compromised accounts have a higher than normal digital exhaust_x000D_
presence? With the patterns learnt from trend analysis, could we build a model to predict which accounts are_x000D_
likely to get compromised? Could the model also give recommendations on how to patch the security problems_x000D_
of accounts that are likely to get compromised?_x000D_
The output of this project includes: (1) a data collection module that can retrieve and store information from_x000D_
publicly available data on compromised accounts from dark web, (2) a set of algorithms that can answer the_x000D_
above-raised questions in trend analysis, and (3) a prediction model that can predict a user's likelihood to be_x000D_
compromised., program purpose french : Computer accounts get compromised for reasons: some used weak passwords; some created a digital exhaust_x000D_
much higher than normal; some were at high-risk positions (e.g., executives, accounting) and were exposed to_x000D_
too many attacks. Learning lessons from compromised accounts and password use would help people design_x000D_
better defense technologies. Following this principle, this Engage project is to develop a set of new algorithms,_x000D_
which automatically learn the trends in compromised accounts and password use and identify potential threats_x000D_
earlier. The project plans to answer the following questions related to computer security: how and where to_x000D_
retrieve relevant information from publicly available data on compromised accounts? How insecure are most_x000D_
passwords? How long would it take to brute force crack/guess them? Do the passwords being used follow any_x000D_
best practice (e.g., letters + numbers)? Are there correlations with password security and the user's professional_x000D_
positions (e.g., executives, managers, and accounting)? Who are the accounts most likely to get compromised_x000D_
and what industry are they in? Do the compromised accounts have a higher than normal digital exhaust_x000D_
presence? With the patterns learnt from trend analysis, could we build a model to predict which accounts are_x000D_
likely to get compromised? Could the model also give recommendations on how to patch the security problems_x000D_
of accounts that are likely to get compromised?_x000D_
The output of this project includes: (1) a data collection module that can retrieve and store information from_x000D_
publicly available data on compromised accounts from dark web, (2) a set of algorithms that can answer the_x000D_
above-raised questions in trend analysis, and (3) a prediction model that can predict a user's likelihood to be_x000D_
compromised., agreement title english : Learning from failure: Trend analysis for compromised accounts and passwords, agreement title french : Learning from failure: Trend analysis for compromised accounts and passwords, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-11-08, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada