Mapping automated cyber attack intelligence to context-based impact on system-level goals

Burnap, Peter ORCID: https://orcid.org/0000-0003-0396-633X, Anthi, Eirini, Reineckea, Philipp, Williams, Lowri, Caoa, Fengnian, Aldmoura, Rakan and Jones, Kevin 2024. Mapping automated cyber attack intelligence to context-based impact on system-level goals. Journal of Cybersecurity and Privacy 4 (2) , pp. 340-356. 10.3390/jcp4020017

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Abstract

Traditionally, cyber risk assessment considers system-level risk separately from individual component-level risk, i.e., devices, data, people. This separation prevents effective impact assessment where attack intelligence for a specific device can be mapped to its impact on the entire system, leading to cascading failures. Furthermore, risk assessments typically follow a failure or attack perspective, focusing on potential problems, which means they need to be updated as attacks evolve. This approach does not scale to modern digital ecosystems. In this paper, we present a Data Science approach, which involves using machine learning algorithms and statistical models to analyse and predict the impact of cyber attacks. Specifically, this approach integrates automated attack detection on specific devices with a systems view of risk. By mapping operational goals in a top-down manner, we transform attack intelligence on individual components into system success probabilities.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	MDPI
ISSN:	2624-800X
Date of First Compliant Deposit:	3 June 2024
Date of Acceptance:	31 May 2024
Last Modified:	12 Jun 2024 13:29
URI:	https://orca.cardiff.ac.uk/id/eprint/169443

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