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Selected Works of Mark Stamp
Contribution to Book
Detecting Encrypted and Polymorphic Malware Using Hidden Markov Models
Guide to Vulnerability Analysis for Computer Networks and Systems — An Artificial Intelligence Approach (2018)
  • D. Dhanasekar, San Jose State University
  • Fabio Di Troia, San Jose State University
  • Katerina Potika
  • Mark Stamp, San Jose State University
Link
Abstract
Encrypted code is often present in some types of advanced malware, while such code virtually never appears in legitimate applications. Hence, the presence of encrypted code within an executable file could serve as a strong heuristic for malware detection. In this chapter, we consider the feasibility of detecting encrypted segments within an executable file using hidden Markov models.
Keywords
  • Encrypted Code,
  • Malware Detection,
  • Metamorphic Viruses,
  • Polymorphic Viruses,
  • Boot Sector
Disciplines
Publication Date
2018
ISBN
978-3-319-92624-7
DOI
10.1007/978-3-319-92624-7_12
Citation Information
D. Dhanasekar, Fabio Di Troia, Katerina Potika and Mark Stamp. "Detecting Encrypted and Polymorphic Malware Using Hidden Markov Models" Guide to Vulnerability Analysis for Computer Networks and Systems — An Artificial Intelligence Approach (2018)
Available at: http://works.bepress.com/mark_stamp/113/