Detecting Industrial Control Malware Using Automated PLC Code Analytics

Author

Zonouz, Saman ; Rrushi, Julian ; McLaughlin, Steve

Author_Institution

Rutgers Univ., Piscataway, NJ, USA

Volume

12

Issue

6

fYear

2014

fDate

Nov.-Dec. 2014

Firstpage

40

Lastpage

47

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.

Keywords

control engineering computing; industrial control; invasive software; production engineering computing; program diagnostics; programmable controllers; safety-critical software; automated PLC code analytics; control theory; hybrid code analytics approach; industrial control malware detection; programmable logic controllers; reverse-engineer safety-critical code; safety engineering; Computer security; Control systems; Energy management; Industrial control; Malware; Model checking; Process control; Reverse engineering; Safety; Safety devices; PLC code analytics; formal models; industrial control malware; model checking; process control systems; reverse engineering; safety-critical code; security;

fLanguage

English

Journal_Title

Security & Privacy, IEEE

Publisher

ieee

ISSN

1540-7993

Type

jour

DOI

10.1109/MSP.2014.113

Filename

7006408