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 |
|---|---|
| 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 |
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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
Cite this
Detecting industrial control malware using automated PLC code analytics. / Zonouz, Saman; Rrushi, Julian; McLaughlin, Stephen.
In: IEEE Security and Privacy, Vol. 12, No. 6, 7006408, 01.11.2014, p. 40-47.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Detecting industrial control malware using automated PLC code analytics
AU - Zonouz, Saman
AU - Rrushi, Julian
AU - McLaughlin, Stephen
PY - 2014/11/1
Y1 - 2014/11/1
N2 - 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.
AB - 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.
KW - formal models
KW - industrial control malware
KW - model checking
KW - PLC code analytics
KW - process control systems
KW - reverse engineering
KW - safety-critical code
KW - security
UR - http://www.scopus.com/inward/record.url?scp=84921416016&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84921416016&partnerID=8YFLogxK
U2 - 10.1109/MSP.2014.113
DO - 10.1109/MSP.2014.113
M3 - Article
VL - 12
SP - 40
EP - 47
JO - IEEE Security and Privacy
JF - IEEE Security and Privacy
SN - 1540-7993
IS - 6
M1 - 7006408
ER -