Keeping the Good Stuff In: Confidential Information Firewalling with the CRM114 Spam Filter & Text Classifier
- Type
- Paper
- Tags
- security
- Authors
- William Yerazunis
- Event
- Black Hat USA 2010
- Indexed on
- Mar 27, 2013
- URL
- https://media.blackhat.com/bh-us-10/whitepapers/Yerazunis/BlackHat-USA-2010-Yerazunis-Confidential-Mail-Filtering-wp.pdf
- File name
- BlackHat-USA-2010-Yerazunis-Confidential-Mail-Filtering-wp.pdf
- File size
- 140.3 KB
- MD5
- 5f4b1bc61ce33098fd7b2dc26c58e83c
- SHA1
- d9bbe7d2ff6e75ad9ec026827b14fd8df8d284db
In this whitepaper we consider the problem of outbound-filtering of emails to prevent accidental leakage of confidential information, We examine how to do this with the GPLed open-source spam filter CRM114 and test the accuracy of this filter against a 10,000+ document corpus of hand-classified emails (both confidential and non-confidential) in Japanese. We look into what moving parts are involved in these filters, and how they can be set up. The results show that a hybrid of multiple CRM114 filters outperforms a human-crafted regular-expression filter by nearly 100x in recall, by detecting > 99.9% of confidential documents, and with a simultaneous false alarm rate of less than 5.3%. As the programmers creating the machine-learning programs don't know how to read or write Japanese, this problem is an almost ideal case of the Searle “Chinese Room” problem.
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