Visible to the public Detecting Cyber Supply Chain Attacks on Cyber Physical Systems Using Bayesian Belief Network

TitleDetecting Cyber Supply Chain Attacks on Cyber Physical Systems Using Bayesian Belief Network
Publication TypeConference Paper
Year of Publication2019
AuthorsYeboah-Ofori, Abel, Islam, Shareeful, Brimicombe, Allan
Conference Name2019 International Conference on Cyber Security and Internet of Things (ICSIoT)
Keywordsartificial intelligence, Bayes methods, Bayesian belief network, Bayesian belief networks, belief networks, Chained Attacks, composability, Computer crime, CPS domain, CSC domain, Cyber Attacks, Cyber physical system, cyber physical systems, cyber security domain, cyber supply chain attacks detection, Cyber Supply Chain Threats, Cyber-physical systems, cyberattack, cyberattack vectors, Cybercrime, cybercrime phenomenon, DAG method, inference mechanisms, invasive software, knowledge representation, power engineering computing, probabilistic inference, pubcrawl, Resiliency, Scalability, smart power grids, Supply chains, threat probability, Uncertainty

Identifying cyberattack vectors on cyber supply chains (CSC) in the event of cyberattacks are very important in mitigating cybercrimes effectively on Cyber Physical Systems CPS. However, in the cyber security domain, the invincibility nature of cybercrimes makes it difficult and challenging to predict the threat probability and impact of cyber attacks. Although cybercrime phenomenon, risks, and treats contain a lot of unpredictability's, uncertainties and fuzziness, cyberattack detection should be practical, methodical and reasonable to be implemented. We explore Bayesian Belief Networks (BBN) as knowledge representation in artificial intelligence to be able to be formally applied probabilistic inference in the cyber security domain. The aim of this paper is to use Bayesian Belief Networks to detect cyberattacks on CSC in the CPS domain. We model cyberattacks using DAG method to determine the attack propagation. Further, we use a smart grid case study to demonstrate the applicability of attack and the cascading effects. The results show that BBN could be adapted to determine uncertainties in the event of cyberattacks in the CSC domain.

Citation Keyyeboah-ofori_detecting_2019