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Breaking and Fixing Content-
Based Filtering
Mayank Dhiman Markus Jakobsson Ting-Fang Yen
Stealth Security Agari/ZapFraud DataVisor
Hi John
CHANGE PASSWORD
https://bit.ly/1PibSU0
Slick logos
Content Based Filtering
• Volume
• Reputation
• Content Signature
• Scam vs Spam
Rise of Targeted Attacks
• Use of Legitimate Infrastructure
• Increase in Attacker Sophistication
• Low Volume
Homograph Attack
• Exploit Gap in Human & Machine “parsing”
• (Ab)use Unicode
Homograph Attack
• Circumvents Signature-based Filters
• ML models trained on “expected input”
Experiment
• Map of confusables (67 in total)
• Transformer Script
• Setup accounts
• Send & Receive Emails
Results
Detection Strategies
• Find “Suspect” Combination of Character Sets
• Map everything to one Character Set
• Count # of transitions of Character Sets and
assign Risk Score:
– High (Words)
– Low (Sentences)
Limitations/Future Work
• Study the effect of fonts, screen size, email
reader
• Repeat for other languages
Questions?

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Breaking and Fixing Content-Based Filtering