Visible to the public Quantitative Risk Assessment of Threats on SCADA Systems Using Attack Countermeasure Tree

TitleQuantitative Risk Assessment of Threats on SCADA Systems Using Attack Countermeasure Tree
Publication TypeConference Paper
Year of Publication2022
AuthorsGao, Xueqin, Shang, Tao, Li, Da, Liu, Jianwei
Conference Name2022 19th Annual International Conference on Privacy, Security & Trust (PST)
Keywordsattack countermeasure tree, compositionality, critical infrastructure, Cyber-physical systems, Power systems, pubcrawl, quantitative assessment, resilience, Resiliency, SCADA, SCADA systems, SCADA Systems Security, security, security management, security threat, Threat Assessment
AbstractSCADA systems are one of the critical infrastructures and face many security threats. Attackers can control SCADA systems through network attacks, destroying the normal operation of the power system. It is important to conduct a risk assessment of security threats on SCADA systems. However, existing models for risk assessment using attack trees mainly focus on describing possible intrusions rather than the interaction between threats and defenses. In this paper, we comprehensively consider intrusion likelihood and defense capability and propose a quantitative risk assessment model of security threats based on attack countermeasure tree (ACT). Each leaf node in ACT contains two attributes: exploitable vulnerabilities and defense countermeasures. An attack scenario can be constructed by means of traversing the leaf nodes. We set up six indicators to evaluate the impact of security threats in attack scenarios according to NISTIR 7628 standard. Experimental results show the attack probability of security threats and high-risk attack scenarios in SCADA systems. We can improve defense countermeasures to protect against security threats corresponding to high-risk scenarios. In addition, the model can continually update risk assessments based on the implementation of the system's defensive countermeasures.
DOI10.1109/PST55820.2022.9851965
Citation Keygao_quantitative_2022