Spam Why Chris Kanich Christian Kreibich

Spam: Why? + = Chris Kanich Christian Kreibich Kirill Levchenko Brandon Enright Vern Paxson Geoffrey M. Voelker Stefan Savage 1

What is Computer security? 2

What is Computer security? • Most of computer science is about providing functionality: u u u u User Interface Software Design Algorithms Operating Systems/Networking Compilers/PL Microarchitecture VLSI/CAD • Computer security is not about functionality • It is about how the embodiment of functionality behaves in the presence of an adversary • Security mindset – think like a bad guy 3

My Background • Collaborative Center for Internet Epidemiology and Defenses (CCIED) u UCSD/ICSI group created in response to worm threat u Very well funded, many strong partners • Goals u u u Internet epidemiology: measuring/understanding attacks Automated defenses: stopping outbreaks/attacks Economic and legal issues: that other stuff

Many big successes… • 50+ papers, lots of tech transfer, big sytems, etc • Network Telescope u Passive monitor for > 1% of routable Internet addr space • Potemkin & GQ Honeyfarms u Active VM honeypot servers on >250 k IP addresses • Earlybird u On-line learning of new worm signatures in < 1 ms

But… depressing truth We didn’t stop Internet worms, let alone malware, let alone cybercrime… nor did anyone else. At best, moved it around a bit. By any meaningful metric the bad guys are winning… Mistake: looking at this solely as a technical problem

Key threat transformations of the 21 st century • Efficient large-scale compromises u u u Internet communications model Software homogeneity User naïveity/fatigue • Centralized control u u Makes compromised host a commodity good Platform economy • Profit-driven applications u u Commodity resources (IP, bandwidth, storage, CPU) Unique resources (PII/credentials, CD-Keys, address book, etc) 7

DDo. S for sale • Emergence of economic engine for Internet crime u SPAM, phishing, spyware, etc • Fluid third party markets for illicit digital goods/services u u Bots ~$0. 5/host, special orders, value added tiers Cards, malware, exploits, DDo. S, cashout, etc.

Botnet Spammer Rental Rates >20 -30 k always online SOCKs 4, url is de-duped and updated > every 10 minutes. 900/weekly, Samples will be sent on > request. Monthly payments arranged at discount prices. • 3. 6 cents per bot week >$350. 00/weekly - $1, 000/monthly (USD) >Type of service: Exclusive (One slot only) >Always Online: 5, 000 - 6, 000 >Updated every: 10 minutes • 6 cents per bot week >$220. 00/weekly - $800. 00/monthly (USD) >Type of service: Shared (4 slots) >Always Online: 9, 000 - 10, 000 >Updated every: 5 minutes • 2. 5 cents per bot week September 2004 postings to Special. Ham. com, Spamforum. biz Bot Payloads 9

Spamalytics 11

Key structural asymmetries • Defenders reactive, attackers proactive u u Defenses public, attacker develops/tests in private Arms race where best case for defender is to “catch up” • New defenses expensive, new attacks cheap u Defenses sunk costs/business model, attacker agile and not tied to particular technology • Low risk to attacker, high reward to attacker u u Minimal deterrence Functional anonymity on the Internet; very hard to fix • Defenses hard to measure, attacks easy to measure u Few security metrics (no “evidence-based” security), attackers measure monetization which drives attack quality 12

Revisiting the problem • We tend to think about this in terms of technical means for securing computer systems • Most of 50 -100 B IT budget on cyber security is spent on securing the end host u u u AV, firewalls, IDS, encryption, etc… Single most expensive front to secure Single hardest front to secure • But are individual end hosts valuable to bad guys? u Maybe $1. 50? Even less in bulk… not a pain point • What instead? Economically informed strategies • • Identify and attack economic bottlenecks in value chain This means understanding the return-on-investment for bad guys 13

Today: the spam problem • We tend to focus on the costs of spam u u u > 100 Billion spam emails sent every day [Ironport] > $1 B in direct costs – anti-spam products/services [IDC] Estimates of indirect costs (e. g. , productivity) 10 -100 x more • But spam exists only because it is profitable • Someone is buying! (though no one has admitted it to me…) • Our goal u Understand underlying economic support for spam 14

History of the spam business model • Direct Mail: origins in 19 th century catalog business u u Idea: send unsolicited advertisements to potential customers Rough value proposition: Delivery cost < (Conversion rate * Marginal revenue) • Modern direct mail (> $60 B in US) u u Response rate: ~2. 5% (mean per DMA) CPM (cost per thousand) = $250 - $1000 • Spam is qualitatively the same… 15

… but quantitatively different • Advantages of e-mail direct marketing u u No printing cost Legitimate delivery cost low (outsourced price ~ $0. 001/message [Get Response]) Dominated by production & lead generation cost (i. e. mailing list) But this is for spam as a legal marketing vehicle… a minority • Spam as marketing/bait for criminal enterprises (scams) u u Mailing lists → ε (purchase/steal/harvest) <$10/M retail Delivery cost → ε (botnet-based delivery) <$70 M retail 16

Anatomy of a modern Pharma spam campaign Courtesy Stuart Brown modernlifisrubbish. co. uk

Estimating spam profits • Recall key basic inequality: (Delivery Cost) < (Conversion Rate) x (Marginal Revenue) • We have some handle on two of these (e. g. , [Franklin 07]) u u • Delivery cost to send spam » Outsourced cost: retail purchase price < $70/M addrs » In-house cost: development/management labor Marginal revenue » Average pharma sale of $100, affiliate commissions ≈ 50% Conversion rate is fundamentally different • We don’t know; estimates vary by orders of magnitude 20

The measurement conundrum • No accident that we lack good conversion measures • Its easy to measure spam from a receiver viewpoint u u u Which MTA sent it to me? What does the content contain? Where do the links go? etc… • But the key economic issue is only known by the sender u Conversion rate * marginal profit = revenue per msg sent • What to do? u u u Interview spammers? (0. 00036) [Carmack 03] Guess? (“millions of dollars a day”) [Corman 08]) Send lots of spam and see who clicks on links? (gold standard) 21

Botnet infiltration • Key idea: distributed C&C is a vulnerability u u u Botnet authors like de-centralized communications for scalability and resilience, but… … to do so, they trust their bots to be good actors If you can modify the right bots you can observe and influence actions of the botnet • Rest of today: preliminary results from a case study u u Infiltrated Storm P 2 P botnet, instrumented ~500 M spams Delivery rates (anti-spam impacts on delivery) Click through (visits to spam advertized sites) Conversions (purchases and purchase amounts) Kanich, Kreibich, Levchenko, Enright, Paxson, Voelker and Savage, Spamalytics: an Empirical Analysis of Spam Marketing Conversion, 22 ACM CCS 2008

How this works in detail • Botnet Infiltration u u Overview of the Storm peer-to-peer botnet » How does Storm work? Mechanics of botnet spamming » How can Storm’s C&C be instrumented? • Economic issues u u Using a botnet for measurement » How to measure conversion via C&C interposition Measuring spam delivery pipeline » What happens to spam from when a bot sends it… » …to when a user clicks “purchase” at a scam site? 23

Storm • Storm is a well-known peer-to-peer botnet • Storm has a hierarchical architecture u u u Workers perform tasks (send spam, launch DDo. S attacks, etc. ) Proxies organize workers, connect to HTTP proxies Master servers controlled directly by botmaster • Workers and proxies are compromised hosts (bots) u u Use a Distributed Hash Table protocol (Overnet) for rendezvous Roughly 20, 000 actives bots at any time in April [Kanich 08] • Master servers run in “bullet-proof” hosting centers u Communicate with proxies and workers via command control (C&C) protocol over TCP Kanich, Levchenko, Enright, Voelker and Savage, The Heisenbot Spamalytics 24 Uncertainty Problem: Challenges in Separating Bots from Chaff, LEET 2008.

Storm architecture Dr. Evil Master servers Proxy bots Worker bots 25

Storm setup • New bots decide if they are proxies or workers u Inbound connectivity? Yes, proxy. No, worker. • Proxies advertise their status via encrypted variant of Overnet DHT P 2 P protocol u u Master sends “Breath of Life” packet to new proxies to tell them IP address of master servers (RSA signature) Allows master servers to be mobile if necessary • Workers use Overnet to find proxies (tricky: time-based key identifies request) • Workers send to proxy, proxy forwards to one of master servers in “safe” data center • Bottom line: imperfect, but remarkably sophisticated 26

Storm spam campaigns l Workers request “updates” to send spam [Kreibich 08] u u Dictionaries: names, domains, URLs, etc. Email templates for producing polymorphic spam » Macros instantiate fields: %^Fdomains^% from domains dict u Lists of target email addresses (batches of 500 -1000 at a time) l Workers immediately act on these updates u u u Create a unique message for each email address Send the message to the target Report the results (success, failure) back to proxies l Many campaign types u Self-propagation malware, pharmaceutical, stocks, phishing, … Kreibich, Kanich, Levchenko, Enright, Voelker, Paxson and Savage, On the Spam Campaign Trail, LEET 2008. 27

Storm templates Macro expansion to insert target email address Example Storm spam template and instantiation 28

Storm in action Received: from dkjs. sgdsz ([132. 233. 197. 74]) by dsl-189 -188 -7963. prod-infinitum. com. mx with Microsoft SMTPSVC(5. 0. 2195. 6713); Received: from auz. xwzww Wed, 6 Feb 2008 16: 33: 44 -0800 ([132. 233. 197. 74]) Received: from auz. xwzww by dsl-189 -188 -79 From: <johnny@hotmail. com> 63. prod-infinitum. com. mx with ([132. 233. 197. 74]) by dsl-189 -188 -79 Received: from %^C 0%^P%^R 2 To: <kreibich@icir. org> Received: from 1224739062~!vern@icir. org 1224720409~!names~!eduardo 1224704030~!pharma_links~! Microsoft SMTPSVC(5. 0. 2195. 6713); 63. prod-infinitum. com. mx with 6^%: qwertyuiopasdfghjklzxcvbnm^%. %^P Subject: Received: from auz. xwzww Say hello to bluepill! Wed, 6 Feb 2008 16: 33: 44 -0800 ckanich@cs. ucsd. edu rafael spammerdomain 1. com %^R 2 Microsoft SMTPSVC(5. 0. 2195. 6713); spammerdomain 3. com P%^R 2([132. 233. 197. 74]) by dsl-189 -188 -796^%: qwertyuiopasdfghjklzxcvbnm^%^% From: <katiera@experimentalist. org> Wed, 6 Feb 2008 16: 33: 44 -0800 savage@cs. ucsd. edu katiera spammerdomain 2. com ([%^C 6%^I^%. %^I^%^%]) by 63. prod-infinitum. com. mx with 6^%: qwertyuiopasdfghjklzxcvbnm^%^% <ckanich@cs. ucsd. edu> To: From: <eduardo@slave. org> %^A^% with Microsoft SMTPSVC(5. 0. 2195. 6713); kreibich@icir. org chris. SMTPSVC(%^Fsvcver^%); %^D^% spammerdomain 3. com ([%^C 6%^I^%. %^I^%^%]) Subject: Say hello. To: bluepill! to <vern@icir. org> Wed, 6 Feb 2008 16: 33: 44 -0800 From: <%^Fnames^%@%^Fdomains^%> spammerdomain 2. com by. . . To: johnny <%^0^%> … %^A^% with Microsoft Subject: Say hello to bluepill! From: <rafael@superlative. edu> spammerdomain 1. com SMTPSVC(%^Fsvcver^%); … Subject: Say hello to bluepill! %^D^% To: savage@cs. ucsd. edu <%^Fpharma_links^%> From: <%^Fnames^%@%^Fdomains^%> Subject: Say hello to bluepill! To: <%^0^%> spammerdomain 2. com Subject: Say hello to bluepill! <%^Fpharma_links^%> 30

Interposition on Storm • We interpose on Storm command control network u Reverse-engineered Storm protocols, communication scrambling, rendezvous mechanisms [Kanich 08] [Kreibich 08] • Run unmodified Storm proxy bots in VMs u Key issue: Real bot workers connect to our proxies • Insert rewriting proxies between workers & proxies u u Transparently interpose on messages between Storm proxies and their associated Storm workers Generic engine for rewriting traffic based on rules • Interpose to control site URLs and spam delivery u u Which sites the spam advertises (replace urls in template links) To whom spam gets sent (replace addrs in target list) 31

Modifying template links Received: from dkjs. sgdsz ([132. 233. 197. 74]) by dsl-189 -188 -7963. prod-infinitum. com. mx with spammerdomain. com Microsoft SMTPSVC(5. 0. 2195. 6713); Wed, 6 Feb 2008 16: 33: 44 -0800 spammerdomain 2. com From: <freebie@pants. com> spammerdomain 3. com To: <ckanich@cs. ucsd. edu> Subject: Say hello to bluepill! newdomain 2. com newdomain 1. com newdomain 2. com newdomain 3. com Received: from dkjs. sgdsz ([132. 233. 197. 74]) by dsl-189 -188 -7963. prod-infinitum. com. mx with Microsoft SMTPSVC(5. 0. 2195. 6713); Wed, 6 Feb 2008 16: 33: 44 -0800 From: <johnny@hotmail. com> To: <kreibich@icir. org> Subject: Say hello to bluepill! spammerdomain 3. com

Measuring click-through • Create two sites that mirror actual sites in spam u u E-card (self-propagation) and pharmaceutical Replace dictionaries with URLs to our sites • E-card (self-prop) site u u Link to benign executable that POSTs to our server Log all POSTs to track downloads and executions • Pharma site u u Log all accesses up through clicks on “purchase” Track the contents of shopping carts • Strive for verisimilitude to remove bias (spam filtering) u Site content is similar, URLs have same format as originals, … 33

Aside: having fun 34

Measuring Delivery • Create various test email accounts u u u At Web mail providers: Hotmail, Yahoo!, Gmail Behind a commercial spam filtering appliance As SMTP sinks: accept every message delivered • Put email addresses in Storm target delivery lists • Log all emails delivered to these addresses u Both labeled as spam (“Junk E-mail”) and in inbox 35

Ethical context • Consequentialism • First, do no harm (users no worse off than before) u We do not send any spam » Proxies are relays, worker bots send spam u We do not enable additional spam to be sent » Workers would have connected to some other proxy u We do not enable spam to be sent to additional users » Users are already on target lists, only add control addresses • Second, reduce harm where possible u Our pharma sites don’t take credit card info u Our e-card sites don’t export malicious code 36

Legal context • Warning: IANAL (we had lawyers involved though) • CAN*SPAM • Subject to strong definition of “initiator”; we don’t fit it • ECPA • Our proxy is directly addressed by worker bots (“party to” communication carve out) • CFAA • We do not contact worker bots, they contact us (“unauthorized access”? ) • We do not cause any information to be extracted or any fundamentally new activity to take place • Hard to find a good theory of damages (functionally indistinguishable -- consequentialism) 37

But… • In this kind of work there is little precedent • No agency to get permission; no way to get indemnity • Lawyers tend to say “I believe this activity has low risk of…” • We communicate our activities to a lot of people • • Security researchers in industry, academia Affected network operators/registrars Law enforcement FTC 38

Aside: Spam is hard • Lots of operational complexities to a study like this • Net Ops notices huge Storm infestation • Address space cleanliness • Registrar issues u u • • Go. Daddy TUCOWS Abuse complaints Spam site support e-mail Anti-virus signatures Law-enforcement 39

Spam conversion experiment • Experimented with Storm March 21 – April 15, 2008 • Instrumented roughly 1. 5% of Storm’s total output Pharmacy Campaign E-card Campaigns Postcard April Fool Worker bots 31, 348 17, 639 3, 678 Emails 347, 590, 389 83, 665, 479 38, 651, 124 Duration 19 days 7 days 3 days 40

Effects rates by country Response of Blacklisting Spam pipeline Feed) (CBL Spam filtering software Sent MTA Inbox Visits Conversions Unused • 347. 5 MThe fraction (24%) 82. 7 M of spam delivered into user(0. 003%) 28 (0. 000008%) 10, 522 inboxes depends on the spam filtering software used 83. 6 M 40. 1 M • u 21. 1 M (25%) 3, 827 (0. 005%) Combination of site filtering (e. g. , blacklists) and 316 (0. 00037%) --content filtering (e. g. , spamassassin) (0. 005%) 225 (0. 00056%) 10. 1 M (25%) 2, 721 Difficult to generalize, but we can use our test accounts for specific services Other filtering Pharma: 12 Two orders of magnitude M spam emails for one “purchase” No large aberrations based on email topic E-card: 1 in 10 that was delivered to the binary Fraction of spam sent visitors execute inboxes Effective 41

The spammer’s bottom line • Recall that we tracked the contents of shopping carts • Using the prices on the actual site, we can estimate the value of the purchases u 28 purchases for $2, 731 over 25 days, or $100/day ($140 active) • We only interposed on a fraction of the workers u u u Connected to approx 1. 5% of workers Back-of-the-envelope (be very careful) $7 -10 k/day for all, or ~$3 M/year With a 50% affiliate commission, $1. 5 M/year revenue • For self-propagation u Roughly 3 -9 k new bots/day 42

Summary • First measurement study of spam marketing conversion • Infiltrated Storm botnet, interposed on spam campaigns u • Pharmaceutical spam u u • Rewriting proxies take advantage of Storm reverse-engineering 1 in 12 M conversion rate $1. 5 M/yr net revenue Profitability possibly tied to infrastructure integration Sent via retail market, this campaign would not be profitable Ergo: in-house delivery (Storm owners = pharma spammers) Self Propagation spam u u 250 k spam emails per infection Social engineering effective: one in ten visitors run executable 43

What are we doing now? • More analysis u u u Extending infiltration to ~15 botnets; comparative analysis Characteristic fingerprints of different spammers/crews Characterizing supply chain relationships » Broadly order on-line “viagra”, rolexes, etc » Cluster credit processor/merchant, mailing materials, etc » Cluster on manufacturing fingerprint (e. g. , NIR spectroscopy) u Measuring monetization by purposely losing credit cards • Proactive defenses u u u Automated filter generation from templates Automated classification of URLs Automated vision-based detection of phishing pages 44

Security courses at UCSD • CSE 107 – Introduction to modern cryptography • CSE 127 – Computer Security • But… • Security plays a role in virtually all of your courses 45

Questions? Collaborative Center for Internet Epidemiology and Defenses http: //ccied. org Yahoo! 46

What’s next: Value-chain characterization • Value-chain characterization u Empirical map establishing links between criminal groups and enablers » Affiliate programs, botnets, fast flux networks, registrars, payment processors, SEO/traffic partners, fulfillment/manufacturing » Data mining across huge data feeds we’ve built or established relationships for u Social network among criminal groups » Semantic Web mining

New: Fulfillment measurements • About to start purchasing wide range of spam-advertized products u Watches u Pharma u Traffic • Cluster purchases based on u Merchant and processor u Packaging (postmark, forensic analysis of paper) u Artifacts of manufacturing process (e. g. , FT-NIR on drugs) 48

New: Bot-based spam filter generation • Observations – Modest number of bots send most spam – Virtually all bots use templates with simple rules to describe polymorphism – random letters and numbers describing spam to be Templates+dictionaries ≈ regex generated – If we can extract or infer these from the botnets, we have a perfect filter for all the spam generated by the botnet – Very specific filters, extremely low FP risk http: //www. marshal. com/trace/spam_statistics. asp phrases from a dictionary

Early results (last week) 0 FP with 50 examples 0 FN on Storm with 500 examples Still tuning for other botnets

Spare slides

Removing crawlers/honeyclients • Anyone can send email to our accounts or visit our Web sites, potentially muddying the waters u Use various heuristics to validate the logs • Validate spam in mailboxes was sent by us u u Spam from other campaigns, bounce messages, etc. Subject line matches our campaign, URL from our dictionary • Validate Web accesses were by users in response u u u Sites with links in spam are immediately crawled by Google, A/V vendors, etc. Special 3 rd-level DNS names, special url encoding Ignore hosts that access robots. txt, don’t load javascript, don’t load flash, don’t load images, many malformed requests 52

Pharma and e-card conversions 53

Who is targeted? l l Top 20 domains l Many Web mail & broadband providers, but very long tail Campaigns have nearly identical distributions l Same scammers, or target lists sold to multiple scammers 54