Mapping Automated Cyber Attack Intelligence to Context-Based Impact on System-Level Goals

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, leadin...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of cybersecurity and privacy 2024-06, Vol.4 (2), p.340-356
Hauptverfasser: Burnap, Pete, Anthi, Eirini, Reineckea, Philipp, Williams, Lowri, Cao, Fengnian, Aldmoura, Rakan, Jones, Kevin
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:2624-800X
2624-800X
DOI:10.3390/jcp4020017