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A practical guide to integration and configuration

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Chapter 13; Improving Filtering

SpamAssassin has a high spam detection rate, but despite this, some spam emails always escape detection. Conversely, legitimate emails are sometimes marked as spam.

This chapter looks at whitelists and blacklists—techniques for spam filtering that mark known good and bad senders. We then discuss the situation where emails have been wrongly classified, and how to resolve this by altering scoring on rules. Finally, we discuss filtering out certain foreign languages and character sets as a method of reducing spam.

Chapter 13: Table of Contents:
- Whitelists and Blacklists
  - Manual Whitelisting and Blacklisting
  - Whitelisting Domains
- The Auto-Whitelist
- Resolving Incorrect Classifications
  - Examining Messages
  - Changing the Spam Threshold
  - Re-weighting Test Scores
    - Increasing the Score of Spam Emails
    - Coping with False Positives
  - Bayesian Unlearning and Relearning
- Character Sets and Languages
  - Disallowing Languages
  - Disallowing Character Sets
- Summary

	BOOK DETAILS
	Paperback, 220 pages Released: Sept 2004 ISBN: 1904811124 Author: Alistair McDonald

TABLE OF CONTENTS

Intro
1. Introducing Spam
2. Spam and Anti-Spam Techniques
3. Open Relays
4. Protecting Email Addresses
5. Detecting Spam
6. Installing SpamAssassin
7. Configuration Files
8. Using SpamAssassin
9. Bayesian Filtering
10. Look and Feel
11. Network Tests
12. Rules
13. Improving Filtering
14. Performance
15. Housekeeping and Reporting
16. Building an Anti-Spam Gateway
17. Email Clients
18. Choosing other Spam Tools
Appendix A
Index

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