Claudiu MUSAT, Ionut GRIGORESCU, Carmen MITRICA, Alexandru TRIFAN

1. Spam Clustering using Wave Oriented K Means Claudiu MUSAT, Ionut GRIGORESCU, Carmen MITRICA, Alexandru TRIFAN

2. You’ll be hearing quite a lot about… • Spam signatures • Previous approaches • Spam Features • Clustering • K-Means • K-Medoids • Stream clustering • Constraints

3. You’ll be hearing quite a lot about… • Spam signatures • Previous approaches • Spam Features • Clustering • K-Means • K-Medoids • Stream clustering • Constraints

4. You’ll be hearing quite a lot about… • Spam signatures • Previous approaches • Spam Features • Clustering • K-Means • K-Medoids • Stream clustering • Constraints

5. And we’ll connect the dots

6. But the essence is… "A nation that forgets its past is doomed to repeat it." Winston Churchill

7. And finally some result charts

8. Spam signatures • Strong relation with dentistry • Necessary Evil ? • Last resort

9. Spam signatures (2) • Most annoying problem is that they are labor intensive • An extension of filtering email by hand • More automation is badly needed to make signatures work

10. Spam features • The ki of the spam business • Its DNA • Everything and yet nothing • Anything that has a constant value in a given spam wave

11. Email Layout • We noticed then that though spammers tend to change everything in an email to conceal the fact that it’s actually spam, they tend to preserve a certain layout. • We encoded the layout of a message in a string of tokens such as 141L2211. • This later evolved in a message summary such as BWWWLWWNWWE • To this day, message layout is the most effective feature • We also use variations of this feature for the MIME parts, for the paragraph contents and so on.

12. Other Spam Features - headers • Subject length, the number of separators, the maximum length of any word • The number of received fields(turned out we were drunk and high when we chose this one) • Whether it had a name in the from field • A quite nice example is the stripped date format • Take the date field • Strip it of all alpha-numeric characters • Store what’s left • “ ,    :: - ()” or “,    :: +” or “,    :: + ” • Any more suggestions?

13. Other Spam Features – body • Its length; the number of lines; whether it has long paragraphs or not; the number of consecutive blank lines; • Basically any part of the email layout that we felt was more important than the average • The number of links/email addresses/phone numbers • Bayes poison • Attatchments • Etc.

14. Combining features (1) • One stick is easy to break • The Roman fasces symbolized power and authority • The symbol of strength through unity from the Roman Empire to the U.S. • The most obvious problem – our sticks are different. • Strings, integers, bools • I’ll stress this later fasces lictoriae (bundles of the lictors)

15. Combining features (2) • If it’s an A and at the same time a B then it’s spam • The idea of combining features never died out • Started with its relaxed form – adding scores • if it has “Viagra” in it – increase its spam score by 10%. • Evolution came naturally National Guard Bureau insignia

16. Why cluster spam? • A “well doh” kind of slide • To extract the patterns we want • How do we combine spam traits to get a reliable spam pattern ? • And which are the traits that matter most? • Agglomerative clustering is just one of many options • Neural Networks • ARTMap worked beautifully on separating ham from spam

17. So why agglomerative? • Because the problem stated before is wrong • We don’t just want spam patterns. • We want patterns for that spam wave alone • Most neural nets make a binary decision. We want a plurality of classes. • Still there are other options, like SVM’s. • They don’t handle well on clustering strings • We want something that accepts just about any feature as long as you can compute a distance

18. K-means and K-medoids • So we chose the simplest of methods – the widely popular K-Means • In a given feature space each item to be classified is a point. • The distance between the points indicates the resemblance of the original items. • From a given set of instances to be clustered, it creates k classes based on their similarity • For spaces where the mean of two points cannot be computed, there is a variety of k-means: k-medoids. • This actually solves the different stick problem • As usual by solving a problem we introduce a whole range of others. • Combining them

19. An Example • Is it a line or a square? • What about string features?

20. Our old model • Focus mainly on correctly defining some powerful spam features • We totally neglected the clustering part • So we used the good old fashioned k-means and k-medoids. • And they have serious drawbacks • A fixed number of classes. • Work only with an offline corpus • The results were... Unpredictable. • Luck played a major role.

21. WOKM – Wave oriented K-Means • By using the simple k-means we could only cluster individual sets of emails • We now needed to cluster the whole incoming stream of spam • We also want to store a history of the clusters we extract • And use that information to detect spam on the user side. • And also to help us better classify in the future • Remember Churchill?

22. WOKM – How does it work ? • Takes snapshots of the incoming spam stream • Takes in only what is new • Train it on those messages • Store the clusters for future reference

23. The spam corpus • All the changes originate here • All messages have an associated distance • The distance from them to the closest stored cluster in the cluster history • New clusters must be closer than old ones • Constrained K-Means • Wagstaff&Cardie, 2001 • “must fit” or “must not fit” • A history constraint

24. The training phase • While a solution has not been found: • Unassigned all the given examples • Assign all examples • Create a given number of clusters • Assign what you can • Create some more and repeat the process • Recompute centers • Merge adjacent(similar) clusters • Counters the cluster inflation brought by the assign phase • Test solution

25. What’s worth remembering • Accepts just about any kind of feature – Booleans, integers and strings. • K-means is limited because you have to know the number of classes a priori. • WOKM determines the optimum number of classes automatically • New messages will not be assigned to clusters that are not considered close enough • Has a fast novelty detection phase, so it can train itself only with new spam. • Can use the triangle inequality to speed things up. • (Future work) Allows us to keep track of the changes spammers make in the design of their products. • By watching clusters that are close to each other

26. Results • Perhaps the most exciting results – the cross language spam clusters

27. Results(2) • Then in spanish • We were surprised to find that this is not an isolated case. YouTube, Microsoft, Facebook fraud attempts also were found in multiple languages

28. Results(3) • Then again in french (different though)

29. And finally the promised charts

30. And finally the promised charts (2)

31. Thank you ! ?