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Identifying Adversarial Cyber-Activity in Operational Technology Environments Using Bayesian Networks

Journal Article · · IEEE Transactions on Information Forensics and Security

Abstract—Critical infrastructure and other operational technology (OT) environments face increasing cybersecurity risks from adversarial behavior. This paper describes the development of a risk model using a Bayesian network to enhance the comprehension of observable cyber events caused by malicious activity in OT environments. The core of the Bayesian network is a process model that describes the stages of adversary behavior. The remainder of the model is based on the MITRE ATT&CK® for Industrial Control Systems (ICS) taxonomy, which includes tactics and techniques that may be used by the adversary. The observables provide evidence for adversary behavior through the intermediary technique and tactic nodes. One challenge in constructing this model is a lack of open-source data from cyber-attacks on OT systems. This paper discusses learning from limited data, the elicitation of expert opinion to construct the conditional probability tables when data is scarce, and the refinement of the most difficult conditional probabilities tables using several forms of sensitivity analyses. Finally, the Bayesian network is demonstrated using two historical case studies: the DarkSide ransomware attack on the Colonial Pipeline and the destructive cyberattack targeting the ThyssenKrupp blast furnace. Index Terms—Cybersecurity, industrial control systems, operational technology

Research Organization:
Idaho National Laboratory (INL), Idaho Falls, ID (United States)
Sponsoring Organization:
USDOE Office of Nuclear Energy (NE); USDOE Office of Cybersecurity, Energy Security, and Emergency Response (CESER)
Grant/Contract Number:
AC07-05ID14517
OSTI ID:
3011940
Report Number(s):
INL/JOU-24-77873
Journal Information:
IEEE Transactions on Information Forensics and Security, Journal Name: IEEE Transactions on Information Forensics and Security Journal Issue: - Vol. 20
Country of Publication:
United States
Language:
English