Visible to the public Application of Bayesian network to data-driven cyber-security risk assessment in SCADA networks

TitleApplication of Bayesian network to data-driven cyber-security risk assessment in SCADA networks
Publication TypeConference Paper
Year of Publication2017
AuthorsHuang, K., Zhou, C., Tian, Y. C., Tu, W., Peng, Y.
Conference Name2017 27th International Telecommunication Networks and Applications Conference (ITNAC)
Date Publishednov
KeywordsAdaptation models, artificial intelligence security, Bayes methods, Bayesian Network, belief networks, Communications technology, computer network security, critical infrastructures, cyber-security, cyber-security risk assessment model, Damage Assessment, Human Behavior, incremental learning, industrial facilities, industrial systems, learning (artificial intelligence), Logic gates, machine learning, Metrics, production engineering computing, pubcrawl, Resiliency, risk assessment, risk management, SCADA network, SCADA network cyber-attacks, SCADA security risk assessment, SCADA systems, SCADA Systems Security, Scalability, security, security risk level, supervisory control and data acquisition

Supervisory control and data acquisition (SCADA) systems are the key driver for critical infrastructures and industrial facilities. Cyber-attacks to SCADA networks may cause equipment damage or even fatalities. Identifying risks in SCADA networks is critical to ensuring the normal operation of these industrial systems. In this paper we propose a Bayesian network-based cyber-security risk assessment model to dynamically and quantitatively assess the security risk level in SCADA networks. The major distinction of our work is that the proposed risk assessment method can learn model parameters from historical data and then improve assessment accuracy by incrementally learning from online observations. Furthermore, our method is able to assess the risk caused by unknown attacks. The simulation results demonstrate that the proposed approach is effective for SCADA security risk assessment.

Citation Keyhuang_application_2017