Causal Discovery of Cyber Attack Phases

Mueller, W. Graham; Memory, Alex; Bartrem, Kyle

doi:10.1109/icmla.2019.00219

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of the time, causality methods are more promising. Mueller, Memory, and Bartrem [40] use causal discovery techniques to discover cyber kill-chains; therefore, data is presented as a Causal Bayesian Network (CBN). Finding the right methodology to explain the experiment's outcome is also part of the development of the CPS-VA and will start as soon as first datasets are ready.…”

Section: Post-run Analysis Methodologymentioning

confidence: 99%

Analyzing Power Grid, ICT, and Market Without Domain Knowledge Using Distributed Artificial Intelligence

Veith¹,

Balduin²,

Wenninghoff³

et al. 2020

Preprint

View full text Add to dashboard Cite

Modern cyber-physical systems (CPS), such as our energy infrastructure, are becoming increasingly complex: An ever-higher share of Artificial Intelligence (AI)-based technologies use the Information and Communication Technology (ICT) facet of energy systems for operation optimization, cost efficiency, and to reach CO 2 goals worldwide. At the same time, markets with increased flexibility and ever shorter trade horizons enable the multi-stakeholder situation that is emerging in this setting. These systems still form critical infrastructures that need to perform with highest reliability. However, today's CPS are becoming too complex to be analyzed in the traditional monolithic approach, where each domain, e.g., power grid and ICT as well as the energy market, are considered as separate entities while ignoring dependencies and side-effects. To achieve an overall analysis, we introduce the concept for an application of distributed artificial intelligence as a self-adaptive analysis tool that is able to analyze the dependencies between domains in CPS by attacking them. It eschews pre-configured domain knowledge, instead exploring the CPS domains for emergent risk situations and exploitable loopholes in codices, with a focus on rational market actors that exploit the system while still following the market rules.

show abstract

Section: Post-run Analysis Methodologymentioning

confidence: 99%