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Machine Learning Methods for Cybersecurity

Machine Learning Methods for Cybersecurity. Jaime G. Carbonell. Mehrbod Sharifi. Eugene Fink. Research goals. Application of machine learning and crowdsourcing to adapt cybersecurity tools to the needs of individual users.

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Machine Learning Methods for Cybersecurity

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  1. Machine Learning Methodsfor Cybersecurity Jaime G.Carbonell MehrbodSharifi EugeneFink

  2. Research goals Application of machine learning and crowdsourcing to adapt cybersecurity tools to the needs of individual users. • Automatically adjust security settings based on personal and contextual information • Apply crowdsourcing to detect “advanced” threats that go beyond software attacks, such as scams, rip-offs, and wrong info

  3. Initial work • Personalized security settings • Help the user with security decisions • Adapt to the user needs and preferences • Crowdsourced threat detection • Offer users the option to enter their opinions and warnings about web pages • Automatically analyze the user opinions and combine them with other indicators

  4. Security problems Inflexibly engineered tools with “too much security” and insufficient customization. • Settings and prompts are confusing for nontechnical users • Many users are unable to customize security tools and always respond yes to prompts For example, 90% ignored the certificate issue of IE7 for banking tasks (Sunshine et al., 09).

  5. Third-party model E User model U User-knowledge model K Task model T Questions H Q S Security-setting model Personalized security settings • Represent relevant data by a set of models • Learn probabilistic graphicalmodel and use inference Start A0. Identify the user and context. A0 E1. Is more information needed? No E1 A1. Collect more observations or ask targeted questions. Yes A1 E2 No E2. Is making decision on behalf of the user possible? Yes History A2 A2. Answer security questions or adjust security settings. A3 A3. Explain the options in more understandable terms. End

  6. PSA: Personal security assistant

  7. Dialog box helper • Record the user responses to dialog boxes • : Make decisions on behalf of the user, based on the learned preferences and the current context • : Provide customizable explanations • Learning from the user behavior • Log the user activity • Transmit the data to the server

  8. Crowdsourced threat detection • Collect metrics for web hosts:IP addresses, whois info, blacklists, … • Aggregate user notes • Enable users to provide notes on their experiences with specific web pages • Summarize available notes • Analyze sentiments and biases • Integrate collected metrics, user-note analysis, and other available indicators

  9. Available at www.cyberpsa.com SmartNotes A browser plug-in for the gathering, sharing, and integration of opinions and warnings about web pages.

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