Trends in Circumventing Web-Malware Detection

UTSA

Moheeb Abu Rajab, Lucas Ballard, Nav Jagpal, Panayiotis Mavrommatis,Daisuke Nojiri, Niels Provos, Ludwig Schmidt

Present by Li Xu

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Detecting Malicious Web Sites

Which pages are safe URLs for end users?

• Safe URL?

• Web exploit?

• Spam-advertised site?

• Phishing site?

URL = Uniform Resource Locator

http://www.bfuduuioo1fp.mobi/ws/ebayisapi.dll

http://fblight.com

http://mail.ru

http://www.sigkdd.org/kdd2009/index.html

This page is reference to Justin Ma’s slides

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Problem in a Nutshell

Different classes of URLs Benign, spam, phishing, exploits, scams... For now, distinguish benign vs. malicious

facebook.com fblight.com

This page is reference to Justin Ma’s slides

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State of the Practice

Current approaches– Virtual Machine Honeypots.– Browser Emulation.– Reputation Based Detection.– Signature Based Detection.

Arms race

How does adversaries respond & what techniques have been

used to bypass detection.

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Google System

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Data Collection

Data Set I, is the data that is generated by ouroperational pipeline, i.e., the output of PageScorer. It was generated by processing 1.6 billion distinct web ∼pages collected be-tween December 1, 2006 and April 1, 2011.

Data Set II,sample pages from data set I suspicious1% of other “non- suspicious” pages uniformly at random from the same time period. rescore the original HTTP responses a fixed version of PageScorer

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Attacks on client honeypot

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Exploits encountered on the web

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Javascript funtion calls

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DOM fuctions

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Malware distribution chain length

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Cloaking sites & 2 methods comparation

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2 methods comparation

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Social Engineering is growing and poses challenges to VM-based honeypots

JavaScript obfuscation that interacts heavily with the DOM can be used to evade both Browser Emulators and AV engines.

AV Engines also suffer significantly from both false positives and false negatives.

Finally, we see a rise in IP cloaking to thwart content-based detection schemes

Summary

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As our analysis is based on sites rather than individual web pages, we compute theaverage value for sites on which we encounter multiple web pages in a given month.

Granularity

UTSA

Thank You

LI XU