Modern society relies on the availability and smooth operation of complex engineering systems, such as electric power systems, water distributions networks, etc. which due to the recent advancements in information and communication technologies (ICT) are usually controlled by means of a cyber-layer. This design may potentially improve the usage of the components of the cyber-physical system (CPS), however further protection is needed due to the emerging threat of cyber-attacks. These may degrade the quality of the communicated information which is of fundamental importance in the decision making process. This paper proposes a novel methodology for automatic identification of the type of the integrity attack affecting a CPS. We designed a feature set for capturing the characteristics of each attack in the spectral and wavelet domains while its distribution is learned by pattern recognition algorithms of different modelling properties customized for the specific application scenario. In addition a novelty detection component is incorporated for dealing with previously unseen types of attacks. The proposed approach is applied onto data coming from the IEEE-9 bus model achieving promising identification performance.

Automatic identification of integrity attacks in cyber-physical systems

NTALAMPIRAS, STAVROS
2016

Abstract

Modern society relies on the availability and smooth operation of complex engineering systems, such as electric power systems, water distributions networks, etc. which due to the recent advancements in information and communication technologies (ICT) are usually controlled by means of a cyber-layer. This design may potentially improve the usage of the components of the cyber-physical system (CPS), however further protection is needed due to the emerging threat of cyber-attacks. These may degrade the quality of the communicated information which is of fundamental importance in the decision making process. This paper proposes a novel methodology for automatic identification of the type of the integrity attack affecting a CPS. We designed a feature set for capturing the characteristics of each attack in the spectral and wavelet domains while its distribution is learned by pattern recognition algorithms of different modelling properties customized for the specific application scenario. In addition a novelty detection component is incorporated for dealing with previously unseen types of attacks. The proposed approach is applied onto data coming from the IEEE-9 bus model achieving promising identification performance.
Critical infrastructure protection; Cyber security; Cyber-physical systems; Deep learning; Fault diagnosis; Probabilistic modelling; Engineering (all); Computer Science Applications1707 Computer Vision and Pattern Recognition; Artificial Intelligence
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1004310
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