Network-Based Spam Filtering Nick Feamster Georgia Tech with Anirudh Ramachandran, Nadeem Syed, Alex...
-
Upload
jayden-moss -
Category
Documents
-
view
212 -
download
0
Transcript of Network-Based Spam Filtering Nick Feamster Georgia Tech with Anirudh Ramachandran, Nadeem Syed, Alex...
Network-Based Spam Filtering
Nick FeamsterGeorgia Tech
with Anirudh Ramachandran, Nadeem Syed, Alex Gray, Sven Krasser, Santosh Vempala
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
One Approach to Mitigation: 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?
4
Content Filtering is Malleable
• 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
5
Sender Reputation: Ephemeral
• Every day, 10% of senders are from previously unseen IP addresses
• Possible causes– Dynamic addressing– New infections
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
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
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
Mail Collection: MailAvenger
• Highly configurable SMTP server• Collects many useful statistics
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 2156282.0.0.0/8 8717
~ 10 minutes
Somewhere between 1-10% of all spam (some clearly intentional,
others might be flapping)
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 won’t be filtered (nice and short)
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
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
What about IP-based blacklists?
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
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
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 blacklistF
ract
ion
of
all
spam
rec
eive
d
Spam from IP-agile senders tend to be listed in fewer blacklists
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
Problem: Changing IP Addresses F
ract
ion
of
IP A
dd
ress
es
About 10% of IP addresses never seen before in trace
20
Under the Radar at Each Domain
Lifetime (seconds)
Am
ou
nt
of
Sp
am
Most spammers send very little spam, regardless of how long they have been spamming.
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 today’s network– Identifying features may be tricky, and some may
require network-wide monitoring capabilities
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
SpamTracker Approach
• Construct a behavioral fingerprint for each sender
• Cluster senders with similar fingerprints
• Filter new senders that map to existing clusters
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
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
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
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
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
Results: ClassificationHam
Spam
SpamTracker Score
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 (85th percentile)
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
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
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
Sender-Receiver Geodesic Distance
90% of legitimate messages travel 2,200 miles or less
35
Density of Senders in IP Space
For spammers, k nearest senders are much closer in IP space
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
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
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
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
Cluster
Classify
IP x domain x time
CollapseLookup Score
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
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
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