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Contribution to Book
Static Analysis of Malicious Java Applets
Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics (2016)
  • Nikitha Ganesh, San Jose State University
  • Fabio Di Troia, Università degli Studi del Sannio
  • Visaggio Aaron Corrado, Università degli Studi del Sannio
  • Thomas H. Austin, San Jose State University
  • Mark Stamp, San Jose State University
Abstract
In this research we consider the problem of detecting malicious Java applets, based on static analysis. Dynamic analysis can be more informative, since it is immune to many common obfuscation techniques, while static analysis is often more efficient, since it does not require code execution or emulation. Consequently, static analysis is generally preferred, provided the results are comparable to those obtained using dynamic analysis. We conduct experiments using three techniques that have been employed in previous studies of metamorphic malware. We show that our static approach can detect malicious Java applets with greater accuracy than previously published research that relied on dynamic analysis.
Keywords
  • Malware,
  • Applets,
  • Hidden Markov Models,
  • Static Analysis,
  • Dynamic Analysis
Publication Date
March 11, 2016
Publisher
ACM
ISBN
978-1-4503-4077-9
DOI
10.1145/2875475.2875477
Citation Information
Nikitha Ganesh, Fabio Di Troia, Visaggio Aaron Corrado, Thomas H. Austin, et al.. "Static Analysis of Malicious Java Applets" New York, NYProceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics (2016) p. 58 - 63
Available at: http://works.bepress.com/mark_stamp/62/