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 | |
| 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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Cite this
Zonouz, S., Rrushi, J., & McLaughlin, S. (2014). Detecting industrial control malware using automated PLC code analytics. IEEE Security and Privacy, 12(6), 40-47. [7006408]. https://doi.org/10.1109/MSP.2014.113