Detecting industrial control malware using automated PLC code analytics

Saman Zonouz; Julian Rrushi; Stephen McLaughlin

doi:10.1109/MSP.2014.113

Detecting industrial control malware using automated PLC code analytics

Saman Zonouz, Julian Rrushi, Stephen McLaughlin

Electrical & Computer Engineering

Research output: Contribution to journal › Article › peer-review

47 Scopus citations

Abstract

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 language	English (US)
Article number	7006408
Pages (from-to)	40-47
Number of pages	8
Journal	IEEE Security and Privacy
Volume	12
Issue number	6
DOIs	https://doi.org/10.1109/MSP.2014.113
State	Published - Nov 1 2014

Keywords

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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10.1109/MSP.2014.113

Cite this

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Detecting industrial control malware using automated PLC code analytics

Abstract

Keywords

ASJC Scopus subject areas

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