1. PPM based Spam Filtering in SEWM2008
Liu JuXin, Xu Congfu, Peng Peng, Lu Guanzhong
College of Computer Science, Zhejiang University
April 10, 2008

2. Outline PPM( prediction by partial matching ) Email Pre-processing
Train PPM Model
Model Classification

3. PPM
Data Compression

4. PPM Framework

5. Email Pre-processing Source alphabet Merge continuous spaces
Truncate long messages

6. Email Pre-processing Sample: Alphabet : {a,b,c,d,e,f,_,=, }
Replace char: ?
Truncate length: 20
Raw Data
Abcd_= Af?/[]=+ safj =ab fe addfe
After Replace
abcd_= ? Af????=? ?af? =ab fe addfe
After Merge Blank
abcd_= ? Af????=? ?af? =ab fe addfe
After Truncate
abcd_= ? Af????=? ?a

7. Train PPM Model Use order-6 PPM* model Use Method D Escape estimation
Train Two PPM model
HAM Model
SPAM Model

8. Model Classification MCE( Minimum Cross-entropy )
MDL( Minimum Description Length )
Spam Score

9. Advantage Simple pre-processing No decode ( avoid obfuscate )
Highly self-adaptive
Low false positive

10. Reference 《Spam Filtering Using Statistical Data Compression Models》
《Unbounded Length Contexts for PPM》

11. Question Delay Index Deliver the filter ham, Ham and HAM
Active learning 10000
Deliver the filter

12. Thanks for your attention! Q&A