1. Application of Machine Learning and Crowdsourcing to Detection of Cyber Threats Jaime G. Carbonell Eugene Fink Mehrbod Sharifi

3. Individual user differences Security needs - Data confidentiality - Data-loss tolerance - Recovery costs Usage patterns Computer knowledge Different users need different security tools.

4. Problems “Advanced user” assumption - Complicated customization - Unclear security warnings Inflexible engineered solutions with “too much security” - Too high security at high costs - Insufficient customization

5. Population statistics Almost everyone uses a computer Most users are naïve, with limited technical knowledge Many security problems are due to the user naïveté

6. Long-term goal We need an intelligent security assistant that... Learns the user needs Detects complex threats Prevents human mistakes Helps the user to apply available security tools

8. Crowdsourcing architecture Identification of web scams Detection of cross-site request forgery Initial results

9. Crowdsourcing architecture Gathering, sharing, and integration of opinions and warnings about web security threats.

10. Crowdsourcing architecture

11. Browser Extension Web Browser Multiple Users Web Service External Data Sources

12. Identification of web scams A web scam is fraudulent or intentionally misleading information posted on the web (e.g. work at home and miracle cures).

13. Identification of web scams Machine learning approach: Collect data about websites, available from various public services Collect human opinions Apply machine learning (currently, logistic regression) to recognize scams based on the available data Accuracy: 98%

14. Detection of cross-site request forgery A cross-site request forgery is an attack through a browser, in which a malicious website uses a trusted session to send unauthorized requests to a target site. Email Malicious Ads News Bank … … … …

15. Detection of cross-site request forgery Machine learning approach: Learn patterns of legitimate requests Detect deviations from these patterns Warn the user about potentially malicious sites and requests

17. Future research... newly evolving threats, not yet addressed by the standard defenses... cyber attacks by their observed “symptoms” in addition to using direct analysis of attacking code... “nontraditional” threats that go beyond malware attacks, such as scams and other social engineering Application of machine learning and crowdsourcing to detect...