The document discusses spam email and various techniques for detecting and filtering spam messages. It begins with a Monty Python sketch about spam food and then discusses how spam costs billions in lost productivity by infecting computers and stealing credentials. It presents various anti-spam methods like pre-sending filtering, new protocols, and increasing spammers' costs. It evaluates supervised machine learning approaches and collective classification, which leverages relationships between documents to improve spam detection without requiring extensive labeling. Evaluation results show collective techniques outperform individual classification. The document concludes by discussing using these approaches to overcome unclassified spam and potentially reduce spam by 95% by disrupting spammers' payment systems.

1. Carlos Laorden

2. WHAT YOU
GOT, THEN? SPAM, EGG,
SPAM, SPAM,
BACON AND
SPAM.
SPAM, SPAM,
SPAM, BAKED
BEANS AND
SPAM.
ANYTHING
WITHOUT
SPAM?
I DON’T
LIKE
SPAM!!
UGH!

3. Meet the real SPiced hAM

4. Monty Python’s Flying Circus

5. Something that repeats and
repeats until being annoying

6. It is a
real problem
for Information Security

7. Billions of daily losses in
productivity

8. Infected computers

9. Stolen credentials

11. We must
fight

12. Anti-spam methods
Pre-sending
New
protocols
Post-sending
Increase sending
costs
Increase risks
for spammers
E-mail
sender
E-mail
content
E-mail
content

13. Usually
supervised
approaches

14. A significant
labelling work
is needed

15. Usually
supervised
approaches
Link structure among documents

16. Collective Classification

17. no interest this
SpamAssassin word has
this has Ling Spam no
interest word
Empirical evaluation
SpamAssassin
Ling Spamt1
t2
t3
D1
D2
D10
D3
D9
D4
D7
D8
D5
D11
D6

18. 0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
10% 20% 30% 40% 50% 60% 70% 80% 90%
Collective KNN, k=10
Collective Forest
Collective Woods
Random Woods
Precision

19. 0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
10% 20% 30% 40% 50% 60% 70% 80% 90%
Collective KNN, k=10
Collective Forest
Collective Woods
Random Woods
Recall

20. 0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
10% 20% 30% 40% 50% 60% 70% 80% 90%
Collective KNN, k=10
Collective Forest
Collective Woods
Random Woods
AUC

21. Suitable to
overcome the amount
of unclassified spam e-mails

22. Will we see
the END of spam?

23. 95%

24. “Solution to spam”
Cut their billing
systems?

28. References
1. Monty Python – Spam:
http://www.youtube.com/watch?v=anwy2MPT5RE
2. Spam wall by freezelight:
http://www.flickr.com/photos/63056612@N00/155554663/
3. monty python flying circus by the_d8_show:
http://www.flickr.com/photos/8056839@N04/478599790/
4. Dollars: http://vegasgravy.com/News-detail/two-women-
caught-for-transporting-drug-money-from-vegas/dollars/
5. Day 97: Infected by dustywrath:
http://www.flickr.com/photos/10921499@N07/2187318683
6. my bank sucks by B Rosen:
http://www.flickr.com/photos/rosengrant/3537904106/
7. Interlinked documents:
http://zhangruiyanz.blogspot.com.es/
8. Honeycomb: http://desktop-
wallpapers.net/3d/Honeycomb.html
9. Feet on table: http://bisystembuilders.com/wp-
content/uploads/2010/02/shutterstock_feet-on-table.jpg
10. Buried on bills: http://getupkids.net/wp-
content/uploads/2013/06/debt_piling.jpg
11. Kill spam: http://www.email-marketing-wizard.com/wp-
content/uploads/2010/03/spammer.jpg