SEAG: A novel dynamic security risk assessment method for industrial control systems with consideration of social engineering

Liu, Kaixiang; Xie, Yongfang; Xie, Shiwen; Sun, Limin

doi:10.1016/j.jprocont.2023.103131

Journal of Process Control

2023

DOI: 10.1016/j.jprocont.2023.103131

|View full text |Cite

SEAG: A novel dynamic security risk assessment method for industrial control systems with consideration of social engineering

Kaixiang Liu,

Yongfang Xie,

Shiwen Xie

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A novel dynamic vulnerability assessment method for Industrial Control System based on vulnerability correlation attack graph

Zhou,

Zhang,

Zhao

et al. 2024

Computers and Electrical Engineering

View full text Add to dashboard Cite

A novel dynamic vulnerability assessment method for Industrial Control System based on vulnerability correlation attack graph

Zhou,

Zhang,

Zhao

et al. 2024

Computers and Electrical Engineering

View full text Add to dashboard Cite

A Security Situation Prediction Model for Industrial Control Network Based on Explainable Belief Rule Base

Li,

Wang,

Yang

et al. 2024

Symmetry

View full text Add to dashboard Cite

Industrial Control Systems (ICSs) are vital components of industrial production, and their security posture significantly impacts operational safety. Given that ICSs frequently interact with external networks, cyberattacks can disrupt system symmetry, thereby affecting industrial processes. This paper aims to predict the network security posture of ICSs to ensure system symmetry. A prediction model for the network security posture of ICSs was established utilizing Evidence Reasoning (ER) and Explainable Belief Rule Base (BRB-e) technologies. Initially, an evaluation framework for the ICS architecture was constructed, integrating data from various layers using ER. The development of the BRB prediction model requires input from domain experts to set initial parameters; however, the subjective nature of these settings may reduce prediction accuracy. To address this issue, an ICS network security posture prediction model based on the Explainable Belief Rule Base (BRB-e) was proposed. The modeling criteria for explainability were defined based on the characteristics of the ICS network, followed by the design of the inference process for the BRB-e prediction model to enhance accuracy and precision. Additionally, a parameter optimization method for the explainable BRB-e prediction model is presented using a constrained Projection Equilibrium Optimization (P-EO) algorithm. Experiments utilizing industrial datasets were conducted to validate the reliability and effectiveness of the prediction model. Comparative analyses indicated that the BRB-e model demonstrates distinct advantages in both prediction accuracy and explainability when compared to other algorithms.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

SEAG: A novel dynamic security risk assessment method for industrial control systems with consideration of social engineering

Cited by 2 publications

References 27 publications

A novel dynamic vulnerability assessment method for Industrial Control System based on vulnerability correlation attack graph

A novel dynamic vulnerability assessment method for Industrial Control System based on vulnerability correlation attack graph

A Security Situation Prediction Model for Industrial Control Network Based on Explainable Belief Rule Base

Contact Info

Product

Resources

About