1. Network-Based Spam Filtering Nick Feamster Georgia Tech with Anirudh Ramachandran, Nadeem Syed, Alex Gray, Sven Krasser, Santosh Vempala

2. 2 Spam: More than Just a Nuisance 75-95% of all email traffic –Image and PDF Spam increasing (PDF spam ~12% and growing) –Content filters cannot catch! As of August 2007, one in every 87 emails constituted a phishing attack Targeted attacks on the rise –20k-30k unique phishing attacks per month –Spam targeted at CEOs, social networks on the rise Source: NetworkWorld, August 2007

3. 3 One Approach to Mitigation: Filtering Prevent unwanted traffic from reaching a users inbox by distinguishing spam from ham Question: What features best differentiate spam from legitimate mail? –Content-based filtering: What is in the mail? –IP address of sender: Who is the sender? –Behavioral features: How the mail is sent?

4. 4 Content Filtering is Malleable Low cost to evasion: Spammers can easily alter features of an emails content can be easily adjusted and changed Customized emails are easy to generate: Content- based filters need fuzzy hashes over content, etc. High cost to filter maintainers: Filters must be continually updated as content-changing techniques become more sophisticated

5. 5 Sender Reputation: Ephemeral Every day, 10% of senders are from previously unseen IP addresses Possible causes –Dynamic addressing –New infections

6. 6 Alternative: Network-Based Filtering Filter email based on how it is sent, in addition to simply what is sent. Network-level properties are more stable –Hosting or upstream ISP (AS number) –Membership in a botnet (spammer, hosting infrastructure) –Network location of sender and receiver –Set of target recipients Challenge: Which properties are most useful for distinguishing spam traffic from legitimate email?

7. 7 Talk Outline Network-level behavior of spammers –Data collection –Highlights Performance of existing sender reputation systems Network-based behavioral filtering techniques –Behavioral blacklisting SpamTracker: Spectral analysis of sender behavior SNARE: Classifier based on lightweight network-level features

8. 8 Data Collection Spam Traps: Domains that receive only spam BGP Monitors: Watch network-level reachability Domain 1 Domain 2 17-Month Study: August 2004 to December 2005

9. 9 Mail Collection: MailAvenger Highly configurable SMTP server Collects many useful statistics

10. 10 BGP Spectrum Agility Log IP addresses of SMTP relays Join with BGP route advertisements seen at network where spam trap is co-located. A small club of persistent players appears to be using this technique. Common short-lived prefixes and ASes 61.0.0.0/8 4678 66.0.0.0/8 21562 82.0.0.0/8 8717 ~ 10 minutes Somewhere between 1-10% of all spam (some clearly intentional, others might be flapping)

11. 11 Why Such Big Prefixes? Flexibility: Client IPs can be scattered throughout dark space within a large /8 –Same sender usually returns with different IP addresses Visibility: Route typically wont be filtered (nice and short)

12. 12 Characteristics of Agile Senders IP addresses are widely distributed across the /8 space IP addresses typically appear only once at our sinkhole Depending on which /8, 60-80% of these IP addresses were not reachable by traceroute when we spot- checked Some IP addresses were in allocated, albeit unannounced space Some AS paths associated with the routes contained reserved AS numbers

13. 13 Other Findings Top senders: Korea, China, Japan –Still about 40% of spam coming from U.S. More than half of sender IP addresses appear less than twice ~90% of spam sent to traps from Windows

14. 14 What about IP-based blacklists?

15. 15 Two Metrics Completeness: The fraction of spamming IP addresses that are listed in the blacklist Responsiveness: The time for the blacklist to list the IP address after the first occurrence of spam

16. 16 Completeness and Responsiveness 10-35% of spam is unlisted at the time of receipt 8.5-20% of these IP addresses remain unlisted even after one month Data: Trap data from March 2007, Spamhaus from March and April 2007

17. 17 Completeness of IP Blacklists ~80% listed on average ~95% of bots listed in one or more blacklists Number of DNSBLs listing this spammer Only about half of the IPs spamming from short-lived BGP are listed in any blacklist Fraction of all spam received Spam from IP-agile senders tend to be listed in fewer blacklists

18. 18 Whats Wrong with IP Blacklists? Based on ephemeral identifier (IP address) –More than 10% of all spam comes from IP addresses not seen within the past two months Dynamic renumbering of IP addresses Stealing of IP addresses and IP address space Compromised machines IP addresses of senders have considerable churn Often require a human to notice/validate the behavior –Spamming is compartmentalized by domain and not analyzed across domains

19. 19 Problem: Changing IP Addresses Fraction of IP Addresses About 10% of IP addresses never seen before in trace

20. 20 Under the Radar at Each Domain Lifetime (seconds) Amount of Spam Most spammers send very little spam, regardless of how long they have been spamming.

21. 21 Where do we go from here? Option 1: Stronger sender identity –Stronger sender identity/authentication may make reputation systems more effective –May require changes to hosts, routers, etc. Option 2: Filtering based on sender behavior –Can be done on todays network –Identifying features may be tricky, and some may require network-wide monitoring capabilities

22. 22 SpamTracker Idea: Blacklist sending behavior (Behavioral Blacklisting) –Identify sending patterns commonly used by spammers Intuition: Much more difficult for a spammer to change the technique by which mail is sent than it is to change the content

23. 23 SpamTracker Approach Construct a behavioral fingerprint for each sender Cluster senders with similar fingerprints Filter new senders that map to existing clusters

24. 24 Building the Classifier: Clustering Feature: Distribution of email sending volumes across recipient domains Clustering Approach –Build initial seed list of bad IP addresses –For each IP address, compute feature vector: volume per domain per time interval –Collapse into a single IP x domain matrix: –Compute clusters

25. 25 Clustering: Output and Fingerprint For each cluster, compute fingerprint vector: New IPs will be compared to this fingerprint IP x IP Matrix: Intensity indicates pairwise similarity

26. 26 Classifying IP Addresses Given new IP address, build a feature vector based on its sending pattern across domains Compute the similarity of this sending pattern to that of each known spam cluster –Normalized dot product of the two feature vectors –Spam score is maximum similarity to any cluster

27. 27 Evaluation Emulate the performance of a system that could observe sending patterns across many domains –Build clusters/train on given time interval Evaluate classification –Relative to labeled logs –Relative to IP addresses that were eventually listed

28. 28 Dataset 30 days of Postfix logs from email hosting service –Time, remote IP, receiving domain, accept/reject –Allows us to observe sending behavior over a large number of domains –Problem: About 15% of accepted mail is also spam Creates problems with validating SpamTracker 30 days of SpamHaus database in the month following the Postfix logs –Allows us to determine whether SpamTracker detects some sending IPs earlier than SpamHaus

29. 29 Results: Classification Ham Spam SpamTracker Score

30. 30 Results: Early Detection Compare SpamTracker scores on accepted mail to the SpamHaus database –About 15% of accepted mail was later determined to be spam –Can SpamTracker catch this? Of 620 emails that were accepted, but sent from IPs that were blacklisted within one month –65 emails had a score larger than 5 (85 th percentile)

31. 31 Evasion Problem: Malicious senders could add noise –Solution: Use smaller number of trusted domains Problem: Malicious senders could change sending behavior to emulate normal senders –Need a more robust set of features…

32. 32 Improving Classification Lower overhead Faster detection Better robustness (i.e., to evasion, dynamism) Use additional features and combine for more robust classification –Temporal: interarrival times, diurnal patterns –Spatial: sending patterns of groups of senders

33. 33 SNARE: Automated Sender Reputation Goal: Sender reputation from a single packet? (or at least as little information as possible) –Lower overhead –Faster classification –Less malleable Key challenge –What features satisfy these properties and can distinguish spammers from legitimate senders

34. 34 Sender-Receiver Geodesic Distance 90% of legitimate messages travel 2,200 miles or less

35. 35 Density of Senders in IP Space For spammers, k nearest senders are much closer in IP space

36. 36 Putting It Together Put features into SVM or decision tree (C4.5) classifier 10-fold cross validation on one day of query logs from a large spam filtering appliance provider

37. 37 Additional History: Message Size Variance Senders of legitimate mail have a much higher variance in sizes of messages they send Message Size Range Certain Spam Likely Spam Likely Ham Certain Ham Surprising: Including this feature (and others with more history) can actually decrease the accuracy of the classifier

38. 38 Deployment Options Integration with existing infrastructure –Deploy SpamTracker as yet another DNSBL –Existing spam filters use SpamTracker score as an additional feature –Advantage: easy deployment On the wire –Infer connections/email from traffic flow records in individual domains –Advantage: Stop mail closer to the source

39. 39 In Progress: Real-Time Blacklist-Style Deployment As mail arrives at servers, lookups received at BL Queries provide proxy for sending behavior Train classifier/cluster based on mail Return current score Approach Email Cluster Classify IP x domain x time Collapse LookupScore

40. 40 Further Challenges Reactivity: Which features be observed quickly enough to construct signatures? Scalability: How to collect and aggregate data, and form the signatures without imposing too much overhead? Reliability: How should the system be replicated to better defend against attack or failure? Sensor placement: Where should monitors be placed to best observe behavior/construct features?

41. 41 Conclusion: Network-Based Behavioral Filtering Spam increasing, spammers becoming agile –Content filters are falling behind –IP-Based blacklists are evadable Up to 30% of spam not listed in common blacklists at receipt. ~20% remains unlisted after a month Complementary approach: behavioral blacklisting based on network-level features –Blacklist based on how messages are sent –SpamTracker: Spectral clustering catches significant amounts faster than existing blacklists –SNARE: Automated sender reputation ~90% accuracy of existing with lightweight features

42. 42 References Anirudh Ramachandran and Nick Feamster, Understanding the Network-Level Behavior of Spammers, ACM SIGCOMM, 2006 Anirudh Ramachandran, Nick Feamster, and Santosh Vempala, Filtering Spam with Behavioral Blacklisting, ACM CCS, 2006 Nadeem Syed, Nick Feamster, Alex Gray and Sven Krasser, SNARE: Spatio-temporal Network-level Automatic Reputation Engine, GT-CSE-08-02