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Selected Works of Thomas H. Austin
Contribution to Book
Malware Detection Using Dynamic Birthmarks
IWSPA '16 Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics (2016)
  • Swapna Vemparala, 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
Link
Abstract
In this paper, we compare the effectiveness of Hidden Markov Models (HMMs) with that of Profile Hidden Markov Models (PHMMs), where both are trained on sequences of API calls. We compare our results to static analysis using HMMs trained on sequences of opcodes, and show that dynamic analysis achieves significantly stronger results in many cases. Furthermore, in comparing our two dynamic analysis approaches, we find that using PHMMs consistently outperforms our technique based on HMMs.
Disciplines
Publication Date
March 11, 2016
Publisher
ACM
ISBN
9781450340779
DOI
10.1145/2875475.2875476
Publisher Statement
SJSU users: use the following link to log in and access the article via SJSU databases.
Citation Information
Swapna Vemparala, Fabio Di. Troia, Visaggio Aaron Corrado, Thomas H. Austin, et al.. "Malware Detection Using Dynamic Birthmarks" New York, NYIWSPA '16 Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics (2016) p. 41 - 46
Available at: http://works.bepress.com/thomas_austin/30/