The document discusses using data mining techniques to analyze email data and identify patterns that can help determine characteristics of users like gender. It describes exploring different features of email data like weight, height, and buttock circumference as proxies for actual user data. Dimensionality reduction techniques are used to visualize email data in fewer dimensions to better identify patterns and natural separations between classes of users. The goal is to improve techniques for determining properties of users from sparse, anonymized data.

1. Finding the Needle in the IP StackDr. Sven KrasserMcAfee, Inc.Session ID: RR-403Session Classification: Intermediate

2. AgendaData Mining – A Human ApproachEnglish WordsBad BehaviorWhat’s in a FileConclusions2

3. Data MiningA Human Approach3

4. Anthropometric Data4Source: http://mreed.umtri.umich.edu/mreed/downloads.html#anthro

5. Measurements5Source: http://mreed.umtri.umich.edu/mreed/downloads.html#anthro

6. Measurements (continueD)6Source: http://mreed.umtri.umich.edu/mreed/downloads.html#anthro

7. 250 –200 –150 –100 –Weight (in pounds)Height Versus Weight60 65 70 75 80Height (in inches)7

8. 250 –200 –150 –100 –WomenWeight (in pounds)Men60 65 70 75 80Height (in inches)Height Versus Weight (continued)8

9. Putting Weight and Height Into Perspective9

10. Best Guess for Gender100% male0% female50% male50% femaleWeight (in pounds)Best Guess0% male100% femaleHeight (in inches)10

11. One Dimension Only0.15 –0.10 –0.05 –0.00 –55 60 65 70 75Height (in inches)11

12. Better Features200 –180 –160 –140 –120 –100 –Weight (in pounds)800 900 1000 1100 1200Buttock Circumference: “The circumference of the body measured at the level of the maximum posterior protuberance of the buttocks.”12

13. Best Guess for Revised Features13Weight (in pounds)Best GuessButtock Circumference

14. Further Improving the SeparationSignal to NoiseFeatures with very different distribution per classCorrelationFeatures with low correlationDimensionalityConsider more features at the same time14

15. Email Data in Three Dimensions15

16. 16Sparse Data25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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17. FinalVerdict+Classification AlgorithmsDecision TreesDecision ForestsSupport Vector MachinesNeural Networks17

18. English WordsAnd why do they look English?18

19. Some English Wordsmilitatecaterwaulderacinatearrantconcinnityimprecationvertiginousprofuse19

20. Some English Explanationsmilitate: to have force or influencecaterwaul: to make a harsh cry or screechderacinate: to uprootarrant: outright; thoroughgoingconcinnity: elegance – used chiefly of literary styleimprecation: a cursevertiginous: causing dizziness; also, giddy; dizzyprofuse: plentiful; copious20Source: http://dictionary.reference.com/

21. Transition Probabilities21

22. Active .com Domains2282 million active .com domains

23. Markov Chains.0073.0641.0213.0912.0912.0732.0014.2175.0143.2626.0301.0939.0322.2419.3598.1457.0633.1064.0588.1733.0872.2738.0431.1534.0932.0714.2936.0437.1860.0196.0371.0291.1932.1120.1269.0411.4759.2979abbnnkkooffppunjjafeerrrryyllineesebbaayuninAnalysis of recent domain registrationsUsing Second Order Markov Chains to detect potentially malicious domain namesbnkofpunjab is not legitimateferrylines.com is legitimateebay.com is not determinable23

24. Limitations of the Markov modelUseful to detect malicious domain namesVery effective for randomly generated namesDetects some legitimate domain names as malicious domainsMalicious names similar to legitimate ones (e.g. ebay.com phishing sites)International domain names and punycodeSolution: add DNS related features into classification process24

25. DNS FeaturesThe number of the nameservers that hosted or are hosting this domain The average time of one nameserver to host this domain The maximum time of one nameserver to host this domain The minimum time of one nameserver to host this domain The number of non-activated nameservers that hosted this domain beforeWhether the domain is an international one25

26. 0.15 –0.10 –0.05 –0.00 –Example FeatureDensity0 200 400 600Time of domain on name server (in days)26

27. 27Results AnalysisTrue Positive RateFalse Positive Rate27

28. Bad BehaviorEmail and Spam28

29. IP Blacklist LookupMail server looks up sender IP over DNSSimple classifier modeled on IP blacklist query logsNarrow data set – queried IP, source IP, timestampDeep data set – billions of query records monthlyMore complex data can be included29

30. Q?Q=xQ?Q=xIP LookupsSenderReceiverDNS Reputation server<Q, S, T>IP=SIP=Q30

31. Feature ExtractionBreadth featuresNumber of messages

32. Number of recipients

33. Burstiness (data transmitted in short, uneven spurts)

34. Sending sessions to individual recipients

35. Global sending sessions to any recipientSpectral featuresPeriodicity over 24-hour window

36. Average and standard deviation of low-frequency discrete Fourier transform (DFT coefficients)

37. Average and standard deviation of high-frequency DFT coefficientsDistribution featuresSource IPs (thousands)31

38. Selection of Advanced FeaturesGeographic features32Static featuresLocation of sender and receiver

39. Distance

40. Local time at sender and receiver

41. Host name features

42. Dial-up Ips

43. Reputation of neighboring IPsContent featuresSparse distribution featuresRatio of good and bad messages

44. Number of “from” domains handled

45. Persistent sender/receiver address pairs

46. Message size distribution