Fighting Spam, Phishing and Online Scams at the Network Level

Fighting Spam, Phishing and Online Scams at the Network Level Nick FeamsterGeorgia Tech with Anirudh Ramachandran, Shuang Hao, Nadeem Syed, Alex Gray, Sven Krasser, Santosh Vempala

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

Filtering • 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?

Conventional Approach: Content Filters • Trying to hit a moving target... Images PDFs Excel sheets ...and even mp3s!

Problems with Content Filtering • Low cost to evasion:Spammers can easily alter features of an email’s 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

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

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

Challenges • 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 • Dynamism: Behavior itself can change • Scale: Lots of email messages (and spam!) out there

Data Collection: 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

Data Collection: MailAvenger • Highly configurable SMTP server • Collects many useful statistics

~ 10 minutes 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)

Why Such Big Prefixes? • Visibility: Route typically won’t be filtered (nice and short) • Flexibility:Client IPs can be scattered throughout dark space within a large /8 • Same sender usually returns with different IP addresses

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

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

What about IP-based blacklists?

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

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

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

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

Ephemeral: Addresses Keep Changing About 10% of IP addresses never seen before in trace Fraction of IP Addresses

Low Volume to Each Domain Most spammers send very little spam, regardless of how long they have been spamming. Amount of Spam Lifetime (seconds)

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 today’s network • Identifying features may be tricky, and some may require network-wide monitoring capabilities

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

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

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

DHCP Reassignment Some Patterns of Sending are Invariant IP Address: 76.17.114.xxx IP Address: 24.99.146.xxx • Spammer's sending pattern has not changed • IP Blacklists cannot make this connection spam spam spam spam spam spam domain3.com domain3.com domain1.com domain2.com domain1.com domain2.com

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

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

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

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

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

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

Classification Results Ham Spam Not always so accurate! SpamTracker Score

Early Detection Results • 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 (85th percentile)

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…

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

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

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

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

Combining Features • Put features into the RuleFit classifier • 10-fold cross validation on one day of query logs from a large spam filtering appliance provider • Using only network-level features • Completely automated

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

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? • Sensor placement: Where should monitors be placed to best observe behavior/construct features?

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

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

Sampling: Training Time

Dynamism: Accuracy over Time

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

Summary: 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 • SpamSpotter: Putting it together in an RBL system

Fighting Spam, Phishing and Online Scams at the Network Level