Attack Detection for Securing Cyber Physical Systems

Weizhong Yan

doi:10.1109/JIOT.2019.2919635

Attack Detection for Securing Cyber Physical Systems

2019-10-01

53

PRE

AI

DOI：10.1109/JIOT.2019.2919635

Original

Original request for help

Save

Abstract

En 中文

Cyber-physical systems (CPSs) security has become a critical research topic as more and more CPS applications are making increasing impacts in diverse industrial sectors. Due to the tight interaction between cyber and physical components, CPS security requires a different strategy from the traditional information technology (IT) security. In this paper, we propose a machine learning-based attack detection (AD) scheme, as part of our overall CPS security strategies. The proposed scheme performs AD at the physical layer by modeling and monitoring physics or physical behavior of the physical asset or process. In developing the proposed AD scheme, we devote our efforts on intelligently deriving salient signatures or features out of the large number of noisy physical measurements by leveraging physical knowledge and using advanced machine learning techniques. Such derived features not only capture the physical relationships among the measurements but also have more discriminant power in distinguishing normal and attack activities. In our experimental study for demonstrating the effectiveness of the proposed AD scheme, we consider heavy-duty gas turbines of combined cycle power plants as the CPS application. Using the data from both the high-fidelity simulation and several real plants, we demonstrate that our proposed AD scheme is effective in early detection of attacks or malicious activities.

Keywords:

Attack detection (AD)

cyber security

cyberphysical system (CPS)

feature engineering

gas turbines

machine learning

Attack Detection for Securing Cyber Physical Systems

Abstract

AI Summary

Journal

Organization