SMART SECURITY ON DEMAND

USING ONLINE ACTIVITY AS DIGITAL FINGERPRINTS TO CREATE A BETTER SPEAR PHISHER Joaquim Espinhara & Ulisses Albuquerque

Agenda

  Introduction   Motivation   Background   HowStuffWorks   µphisher   Demo   Future Work   Conclusion

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About Us

  Joaquim Espinhara –  Security Consultant at Trustwave Spiderlabs

  Ulisses Albuquerque –  Coder for offense & defense… as long as it’s fun! –  Lab Manager at Trustwave Spiderlabs

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INTRODUCTION

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OUR MOTIVATION

Our Motivation

  Why?   Tools available

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BACKGROUND

Background

  Social Networks   Social Engineering   Data Pre-processing   Natural Language Processing (NLP)

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Background

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Social Networks

Facebook

Twitter

Linkedin

Others

Background

  Communication channel for keeping in touch with someone (Facebook, Twitter)

  Media sharing (Instagram)   Specialized networks (GetGlue, TripIt, LastFM, Linkedin)

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Social Networks

Background

  Phishing

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Social Engineering

Background

  What is it?   How do we use it?

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Data Pre-processing

Background

  Data Pre-processing Flow

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Data Pre-processing

Raw data set

"Had lunch with @urma and

@jespinhara today #tgif #lunch"

Data cleaning

"Had lunch with @urma and @jespinhara

today"

Data integration

"Had lunch with @urma and @jespinhara

today"

Data normalization

"Had lunch with @urma and

@jespinhara today (2013-06-05)"

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HOWSTUFFWORKS

HOWSTUFFWORKS

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Our Approach

Identifying the subject to profile

Collecting social

network data

Analyzing and building the profile

HOWSTUFFWORKS – Our Approach

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The Unknown Subject (Unsub)

Joaquim Espinhara

(Target)

@jespinhara (Twitter)

joaquim.espinhara (Facebook)

uid=12345 (LinkedIn)

HOWSTUFFWORKS – Our Approach

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Data Collection

  Social Network IDs   Official APIs   Web Scraping   OAuth

HOWSTUFFWORKS – Our Approach

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Data Collection - Twitter

Application ID (µphisher)

User ID (@jespinhara)

Twitter @urma @effffn

@SpiderLabs

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µPHISHER

µphisher

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Reference implementation

  Goals –  Validate potential unsub content (Defense) –  Assisted textual content input (Attack)

µphisher

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Reference implementation

  Web Application   Console Application   Twitter only (for now)   Open Source (GPLv3)

µphisher

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Reference implementation

µphisher Ruby on Rails

MongoDB + Mongoid

DelayedJob

OAuth

treat (Stanford

NLP)

Unsub Registration Demo

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µphisher

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Reference implementation

Authentication Unsub Registration

Data Source Registration Data Collection Work Set

Definition Work Set Analysis Unsub Profile

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DEMO (FINGERS CROSSED)

New Profile Demo

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Assisted Input Demo

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µphisher

  How to use forged content?

Personal email screenshot

µphisher – Future Work

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Coming Soon

  Support for additional data sources   Topic clustering customized per data source   More metrics and feedback for assisted input   Usability (UX help very much needed)

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CONCLUSION

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HTTPS://GITHUB.COM/URMA/MICROPHISHER Download, test, & contribute – any feedback is welcome

Thank You!