Home // CYBER 2024, The Ninth International Conference on Cyber-Technologies and Cyber-Systems // View article
Authors:
Fatemeh Movafagh
Uwe Glässer
Keywords: Cyber Resilience; Critical Infrastructure Security; Cyber-Physical Systems; Supervisory Control and Data Acquisition (SCADA); Online Threat Detection; Bayesian Inference, Anomaly Detection; Suspicious Activity Markers (SAMs); Machine Learning.
Abstract:
The increasing sophistication and evolving nature of cyber threats pose significant risks to critical infrastructure systems. This research introduces GenAttackTracker, a novel algorithmic framework designed for real-time detection and interpretation of cyber threats in Supervisory Control and Data Acquisition (SCADA) systems. By integrating dynamic anomaly scoring with hierarchical Bayesian modeling, GenAttackTracker enhances situational awareness for identifying potential security breaches in operational technology environments. This robust mechanism contributes directly to enhancing cyber resilience by improving threat detection in critical infrastructure systems, an essential component of ensuring the continuity and security of mission-critical processes. The framework leverages primary data from SCADA systems and secondary contextual data sources, termed Suspicious Activity Markers (SAMs). Through Bayesian inference, the model continuously updates its understanding of the system's security status, allowing informed decision-making.
Pages: 38 to 44
Copyright: Copyright (c) IARIA, 2024
Publication date: September 29, 2024
Published in: conference
ISSN: 2519-8599
ISBN: 978-1-68558-186-2
Location: Venice, Italy
Dates: from September 29, 2024 to October 3, 2024