Rustock Botnet and ASNs

Rustock Botnet and ASNs

TPRC 24 September 2011

John S. Quarterman, Quarterman CreationsSerpil Sayin, Koç University

Andrew B. Whinston, U. Texas at Austin

Supported by NSF grant no. 0831338; the usual disclaimers apply.

Spam, Botnets, Security, and Policy

Starting with some published ASN rankings Drill down to Rustock and other botnets Show some effects of a takedown Specific enough to be actionable affected orgs They could use to detect and fix vulnerabilities How to get the orgs to pay attention? Reputational rankings to produce peer pressure A few simple policy suggestions

After the Rustock Takedown

Rustock Takedown and Slowdown

December 2011 Rustock Slowdown 16 March 2011 Rustock Takedown Which ASNs were affected? Effects on overall spam? Using data from CBL blocklist To ASNs and orgs using Team Cymru data Rankings and graphs by SpamRankings.net

Rustock Takedown Rank Effects

16 March 2011 Takedown Daily Graph

Takedown

HardwareOutage

December 2011 Rustock Slowdown

Slowdown

Dec 2010 – July 2011 Top Botnets Recovery

Slowdown

Takedown

Slowdown vs. Takedown

Slowdown: gradual and temporary During slowdown:

Maazben and bobax took up the slack As Rustock returned in Jan, bobax went back down

After slowdown: Maazben also retreated to its old levels Rustock #1, Lethic #2

Takedown: rapid and much longer-lasting But other botnets took up the slack

Dec 2010 Top Spamming ASNs

Increases

During

slowdown

March 2011 Top Spamming ASNs

4766 #1

4766 #9

March 2011 AS 4766's Botnets

Lethic

Dec 2010 AS 9829's Botnets

Bobax

Lethic

March 2011 Top Botnets

Lethic

Maazben

Opportunistic Botnets & Spamming

Knock one down Two more pop up Spammers can just rent from a different botnet Other botnets can use same vulnerabilities

Dec 2010 Top Botnets

Rustock

Lethic

Congratulations Rustock Takedown!

Takedown had more lasting effect than Slowdown

Congratulations! But in both cases other botnets started to take

up the slack Whack-a-mole is fun, but not a solution Need many more takedowns Or many more organizations playing How do we get orgs to do that?

Cyberwar meets IT Security

Generations of warfare 1st: massed troops 2nd tanks and heavy

artillery 3rd maneuver 4th IEDs and suicide

bombs 5th open source gangs

in it for the money

Cyberwarfare responses 1st: key escrow 2nd Internet off switch at

CONUS (Maginot Line) 3rd CERT, FIRST, etc. 4th botnet takedowns 5th economic and

reputational incentives for distributed diverse commons governance

Spam as a Proxy for Infosec

Most orgs keep security problems secret Because they think it will harm their reputation Ahah! Publish reputation and they'll care Need available proxy for security Anti-spam blocklists have spam data Spam comes from botnets which use vulns Just as a sneeze means disease, outbound spam

means poor infosec (Other diseases may not sneeze; for those other

data; come back to that later.)

Peer pressure and Medical orgs

Peer pressure is key: rank similar orgs (Festinger, Luttmer, Apesteguia; see paper for refs)

Spam data is for every org on the Internet, not just ISPs; any ESP (Email Service Provider)

We ranked medical orgs (worldwide, U.S.) Within 2 months they all dropped to zero spam Confirmation from [confidential] medical org: 'The listing on your site added additional impetus to

make sure we “stay clean” so in that regard, you are successful.'

How Rankings Work

Rankings must be: Frequent, comprehensive, and detailed Must compare peers

To be usable: Marketing: brag about good rankings; bad rankings

are incentive to get better so can brag Sales: good reputation for customer retention Diagnostics: drilldowns for clues to what to fix

Producing more comprehensive application of existing Internet security methods

Many rankings examples

FT Business school rankings Vehicle Blue Book Credit rating: Moody, S&P, Fitch And by far the most numerous: sports scores

In leagues, for teams, for players Detailed, earned run average, etc. And composite overall

Further rankings from spam data

Botnet rankings: botnets use known vulnerabilities; orgs infested by botnets prob. have those vulns; not good for their reputation

Vulnerability rankings: an org infested by several botnets which exploit common vulns very likely has those vulns

Infosec experiments: an org can change its infosec and watch rankings to see which infosec works

Single IP address drilldowns: which addresses are spamming, which botnets infest them

Derivative rankings

Normalized (addresses, customers, employees) Susceptibility (speed of infection by botnets) Recidivism (frequency of re-infestation) Improvement (change over time) Composite (weighted average of all the above)

Internet field experiments

We are releasing rankings for one country Then later for a similar country Does the second country change similarly? Can experiment with many rankings Per country, per org category, per data source Does peer pressure on disclosure change

behavior? The rankings themselves provide ways to

determine how well they work

Policy: other data, other rankings SpamRankings.net pioneers reputational peer

rankings related to Internet security Available now because spam data is available Similar rankings could be made with other data:

Phishing sources and servers Breaches, vulns, etc.: you can think of more

A simple policy suggestion: Require making other specific data available Enable multiple rankings by multiple agencies Transparency for diverse cooperation (Elinor Ostrom)

Needed and Not Needed NeededNeeded More data sources Publicly available Frequent, comprehensive More research (data

correlation, ranking effects, law, policy, etc.)

Independent ranking and certification agency(ies)

Many diverse, cooperating entities (rankers, ranked, academia, industry, govt)

Not neededNot needed New Internet

protocols Punitive laws Reports only to govt Sporadic reports Selected by

reporting orgs Dept. of Homeland

Internet Security

Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant No. 0831338. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

We also gratefully acknowledge custom data from CBL, PSBL, Fletcher Mattox and the U. Texas Computer Science Department, Quarterman Creations, Gretchen Phillips and GP Enterprise, and especially Team Cymru. None of them are responsible for anything we do, either.

Contact

iiaradmin@utlists.utexas.edu

antispam@quarterman.com