Network-Level Spam Defenses

1. Network-Level Spam Defenses Nick FeamsterGeorgia Tech with Anirudh Ramachandran, Shuang Hao, Alex Gray, Santosh Vempala

2. Spam: More than Just a Nuisance • 95% of all email traffic • Image and PDF Spam (PDF spam ~12%) • 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 Source: CNET (January 2008), APWG

3. Approach: Filter • Prevent unwanted traffic from reaching a user’s 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. Conventional: Content Filters • Trying to hit a moving target... Images PDFs Excel sheets ...and even mp3s!

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

6. Another Approach: IP Addresses • Problem: IP addresses are ephemeral • Every day, 10% of senders are from previously unseen IP addresses • Possible causes • Dynamic addressing • New infections

7. Our Idea: Network-Based Filtering • Filter email based on how it is sent, in addition to simply whatis sent. • Network-level properties are less malleable • Network/geographic location of sender and receiver • Set of target recipients • Hosting or upstream ISP (AS number) • Membership in a botnet (spammer, hosting infrastructure)

8. Why Network-Level Features? • Lightweight: Don’t require inspecting details of packet streams • Can be done at high speeds • Can be done in the middle of the network • Robust:Perhaps more difficult to change some network-level features than message contents

9. Challenges (Talk Outline) • Understandingnetwork-level behavior • What network-level behaviors do spammers have? • How well do existing techniques work? • Building classifiers using network-level features • Key challenge: Which features to use? • Two Algorithms: SNARE and SpamTracker • Building the system • Dynamism: Behavior itself can change • Scale: Lots of email messages (and spam!) out there

10. Some Questions of Study • Where (in IP space, in geography) does spam originate from? • What OSes are used to send spam? • What techniques are used to send spam?

11. Data: Spam and BGP • 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

12. Data Collection: MailAvenger • Configurable SMTP server • Collects many useful statistics

13. ~ 10 minutes Finding: BGP “Spectrum Agility” • Hijack IP address space using BGP • Send spam • Withdraw IP address 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 Somewhere between 1-10% of all spam (some clearly intentional, others might be flapping)

14. Spectrum Agility: 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 won’t be filtered (nice and short)

15. 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

16. How Well do IP Blacklists Work? • 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

17. 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

18. What’s 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. Are There Other Approaches? • Option 1: Stronger sender identity [AIP, Pedigree] • Stronger sender identity/authentication may make reputation systems more effective • May require changes to hosts, routers, etc. • Option 2: Behavior-based filtering [SNARE, SpamTracker] • Can be done on today’s network • Identifying features may be tricky, and some may require network-wide monitoring capabilities

20. Outline • Understanding the network-level behavior • What behaviors do spammers have? • How well do existing techniques work? • Classifiers using network-level features • Key challenge: Which features to use? • Two algorithms: SNARE and SpamTracker • The System: SpamSpotter • Dynamism: Behavior itself can change • Scale: Lots of email messages (and spam!) out there

21. Finding the Right Features • Goal: Sender reputation from a single packet? • Low overhead • Fast classification • In-network • Perhaps more evasion resistant • Key challenge • What features satisfy these properties and can distinguish spammers from legitimate senders?

22. Set of Network-Level Features • Single-Packet • AS of sender’s IP • Distance to k nearest senders • Status of email service ports • Geodesic distance • Time of day • Single-Message • Number of recipients • Length of message • Aggregate (Multiple Message/Recipient)

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

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

25. Local Time of Day at Sender Spammers “peak” at different local times of day

26. Combining Features: RuleFit • Put features into the RuleFit classifier • 10-fold cross validation on one day of query logs from a large spam filtering appliance provider • Comparable performance to SpamHaus • Incorporating into the system can further reduce FPs • Using only network-level features • Completely automated

27. SNARE: Putting it Together • Email arrival • Whitelisting • Top 10 ASes responsible for 43% of misclassified IP addresses • Greylisting • Retraining

28. Benefits of Whitelisting Whitelisting top 50 ASes:False positives reduced to 0.14%

29. Outline • Understanding the network-level behavior • What behaviors do spammers have? • How well do existing techniques work? • Classifiers using network-level features • Key challenge: Which features to use? • Algorithms: SNARE andSpamTracker • Building the system (SpamSpotter) • Dynamism: Behavior itself can change • Scale: Lots of email messages (and spam!) out there

30. 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

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

32. DHCP Reassignment Infection SpamTracker: Identify Invariant IP Address: 24.99.146.xxx Unknown sender IP Address: 76.17.114.xxx Known Spammer spam spam spam spam spam spam domain3.com domain1.com domain2.com domain3.com domain1.com domain2.com Cluster on sending behavior Cluster on sending behavior Similar fingerprint! Behavioral fingerprint

33. 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

34. 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

35. 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

36. Data • 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

37. Clustering Results Ham Spam Separation may not be sufficient alone, but could be a useful feature SpamTracker Score

38. Outline • Understanding the network-level behavior • What behaviors do spammers have? • How well do existing techniques work? • Building classifiers using network-level features • Key challenge: Which features to use? • Algorithms: SpamTracker and SNARE • Building the system (SpamSpotter) • Dynamism: Behavior itself can change • Scale: Lots of email messages (and spam!) out there

39. Deployment: Real-Time Blacklist • As mail arrives, lookups received at BL • Queries provide proxy for sending behavior • Train based on received data • Return score Approach

40. Challenges • Scalability: How to collect and aggregate data, and form the signatures without imposing too much overhead? • Dynamism: When to retrain the classifier, given that sender behavior changes? • Reliability: How should the system be replicated to better defend against attack or failure? • Evasion resistance: Can the system still detect spammers when they are actively trying to evade?

41. Design Choice: Augment DNSBL • Expressive queries • SpamHaus: $ dig 55.102.90.62.zen.spamhaus.org • Ans: 127.0.0.3 (=> listed in exploits block list)‏ • SpamSpotter: $ dig \ receiver_ip.receiver_domain.sender_ip.rbl.gtnoise.net • e.g., dig 120.1.2.3.gmail.com.-.1.1.207.130.rbl.gtnoise.net • Ans: 127.1.3.97 (SpamSpotter score = -3.97)‏ • Also a source of data • Unsupervised algorithms work with unlabeled data

42. Latency Performance overhead is negligible.

43. Design Choice: Sampling Relatively small samples can achieve low false positive rates

44. Possible Improvements • Accuracy • Synthesizing multiple classifiers • Incorporating user feedback • Learning algorithms with bounded false positives • Performance • Caching/Sharing • Streaming • Security • Learning in adversarial environments

45. Summary: Network-Based Behavioral Reputation • 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 • Key idea: Blacklist based on how messages are sent • SNARE: Automated sender reputation • ~90% accuracy of existing with lightweight features • SpamTracker: Spectral clustering • catches significant amounts faster than existing blacklists • SpamSpotter: Putting it together in an RBL system

46. Next Steps: Phishing and Scams • Scammers host Web sites on dynamic scam hosting infrastructure • Use DNS to redirect users to different sites when the location of the sites move • State of the art: Blacklist URL • Our approach: Blacklist based on network-level fingerprints Konte et al., “Dynamics of Online Scam Hosting Infrastructure”, PAM 2009

47. 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, 2007 • Nadeem Syed, Shuang Hao, Nick Feamster, Alex Gray and Sven Krasser, “SNARE: Spatio-temporal Network-level Automatic Reputation Engine”, GT-CSE-08-02 • Anirudh Ramachandran, Shuang Hao, Hitesh Khandelwal, Nick Feamster, Santosh Vempala, “A Dynamic Reputation Service for Spotting Spammers”, GT-CS-08-09 (In submission)

48. Time Between Record Changes Fast-flux Domains tend to change much more frequently than legitimately hosted sites

50. 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