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Panorama: capturing system-wide information flow for malware detection and analysis
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Conference on Computer and Communications Security archive
Proceedings of the 14th ACM conference on Computer and communications security table of contents
Alexandria, Virginia, USA
SESSION: Operating systems and malware table of contents
Pages: 116 - 127  
Year of Publication: 2007
ISBN:978-1-59593-703-2
Authors
Heng Yin  Canegie Mellon University, Pittsburgh, PA
Dawn Song  University of California at Berkeley, Berkeley, CA
Manuel Egele  Technical University Vienna, Vienna, Austria
Christopher Kruegel  Technical University Vienna, Vienna, Austria
Engin Kirda  Technical University Vienna, Vienna, Austria
Sponsors
ACM: Association for Computing Machinery
SIGSAC: ACM Special Interest Group on Security, Audit, and Control
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 69,   Downloads (12 Months): 440,   Citation Count: 18
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ABSTRACT

Malicious programs spy on users' behavior and compromise their privacy. Even software from reputable vendors, such as Google Desktop and Sony DRM media player, may perform undesirable actions. Unfortunately, existing techniques for detecting malware and analyzing unknown code samples are insufficient and have significant shortcomings. We observe that malicious information access and processing behavior is the fundamental trait of numerous malware categories breaching users' privacy (including keyloggers, password thieves, network sniffers, stealth backdoors, spyware and rootkits), which separates these malicious applications from benign software. We propose a system, Panorama, to detect and analyze malware by capturing this fundamental trait. In our extensive experiments, Panorama successfully detected all the malware samples and had very few false positives. Furthermore, by using Google Desktop as a case study, we show that our system can accurately capture its information access and processing behavior, and we can confirm that it does send back sensitive information to remote servers in certain settings. We believe that a system such as Panorama will offer indispensable assistance to code analysts and malware researchers by enabling them to quickly comprehend the behavior and innerworkings of an unknown sample.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
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Bochs: The open source IA-32 emulation project. http://bochs.sourceforge.net/.
 
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D. Brumley, C. Hartwig, M. G. Kang, Z. Liang, J. Newsome, D. Song, and H. Yin. BitScope: Automatically dissecting malicious binaries. Technical Report CMU-CS-07-133, School of Computer Science, Carnegie Mellon University, March 2007.
 
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D. Brumley, C. Hartwig, Z. Liang, J. Newsome, D. Song, and H. Yin. Botnet Analysis, chapter Automatically Identifying Trigger-based Behavior in Malware. 2007.
 
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Google Desktop - Privacy Policy. http://desktop.google.com/en/privacypolicy.html.
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J. Rutkowska. System virginity verifier: Defining the roadmap for malware detection on windows systems. In Hack In The Box Security Conference, September 2005. http://www.invisiblethings.org/papers/hitb05_virginity_verifier.ppt.
 
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The Sleuth Kit (TSK). http://www.sleuthkit.org/sleuthkit/.
 
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P. Vogt, F. Nentwich, N. Jovanovic, E. Kirda, C. Kruegel, and G. Vigna. Cross-Site Scripting Prevention with Dynamic Data Tainting and Static Analysis. In Proceeding of the Network and Distributed System Security Symposium (NDSS '07), February 2007.
 
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CITED BY  18

Collaborative Colleagues:
Heng Yin: colleagues
Dawn Song: colleagues
Manuel Egele: colleagues
Christopher Kruegel: colleagues
Engin Kirda: colleagues