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Personalized Cybersecurity
for Dummies
Jaime G.
Application of machine learning
and crowdsourcing to adapt
cybersecurity tools to the needs
of (naïve) individual users.
Individual user differences
• Security needs
- Data confidentiality
- Data-loss tolerance
- Recovery costs
• Usage patterns
• Computer knowledge
Different users need
different security tools.
• Inflexible engineered solutions
with “too much security”
- Too high security at high costs
- Insufficient customization
• “Advanced user” assumption
- Complicated customization
- Unclear security warnings
Typical response of naïve users:
• Always no (too much security)
• Always yes (not enough security)
• Ask a techie if available
Population statistics
Computer use by
age and gender
User naïveté
Population statistics
• Almost everyone uses a computer
• Most users are naïve, with very
limited technical knowledge
• Many security problems are
due to the user naïveté
When an average user deals with
security issues, she needs basic
advice and handholding.
Long-term goal
We need an automated security
assistant that learns the needs
of the individual user and helps
the user to apply security tools.
Initial results
A security assistant for
web browsing, integrated
with Internet Explorer.
More problems
Automated tools cannot detect
“advanced” threats that go
beyond software attacks.
• Scams (welcome to Nigeria)
• Rip-offs (overpricing, low quality)
• Bad info (inaccurate, biased)
• ... and so on
Long-term goal
Rely on the collective
wisdom of the users.
Initial results
A browser plug-in for the
gathering of opinions and
warnings about web pages.
Future research
• Summarization of comments
• Analysis of sentiments and biases
• Identification of reliable contributors
• Synergy with other techniques for
analysis of web pages
• … and so on