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Breaking and Fixing Content-Based Filtering

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Mayank Dhiman

Paper resented at APWG's eCrime 2017 conference in Scottsdale, Arizona

Science
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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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