Detecting industrial control malware using automated PLC code analytics

Saman Zonouz, Julian Rrushi, Stephen McLaughlin

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


The authors discuss their research on programmable logic controller (PLC) code analytics, which leverages safety engineering to detect and characterize PLC infections that target physical destruction of power plants. Their approach also draws on control theory, namely the field of engineering and mathematics that deals with the behavior of dynamical systems, to reverse-engineer safety-critical code to identify complex and highly dynamic safety properties for use in the hybrid code analytics approach.

Original languageEnglish (US)
Article number7006408
Pages (from-to)40-47
Number of pages8
JournalIEEE Security and Privacy
Issue number6
StatePublished - Nov 1 2014


  • formal models
  • industrial control malware
  • model checking
  • PLC code analytics
  • process control systems
  • reverse engineering
  • safety-critical code
  • security

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Law


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