A review of cyber security risk assessment methods for SCADA systems

This paper reviews the state of the art in cyber security risk assessment of Supervisory Control and Data Acquisition (SCADA) systems. We select and in-detail examine twenty-four risk assessment methods developed for or applied in the context of a SCADA system. We describe the essence of the methods and then analyse them in terms of aim; application domain; the stages of risk management addressed; key risk management concepts covered; impact measurement; sources of probabilistic data; evaluation and tool support. Based on the analysis, we suggest an intuitive scheme for the categorisation of cyber security risk assessment methods for SCADA systems. We also outline five research challenges facing the domain and point out the approaches that might be taken

Cyber physical security of avionic systems

“Cyber-physical security is a significant concern for critical infrastructures. The exponential growth of cyber-physical systems (CPSs) and the strong inter-dependency between the cyber and physical components introduces integrity issues such as vulnerability to injecting malicious data and projecting fake sensor measurements. Traditional security models partition the CPS from a security perspective into just two domains: high and low. However, this absolute partition is not adequate to address the challenges in the current CPSs as they are composed of multiple overlapping partitions. Information flow properties are one of the significant classes of cyber-physical security methods that model how inputs of a system affect its outputs across the security partition. Information flow supports traceability that helps in detecting vulnerabilities and anomalous sources, as well as helps in rendering mitigation measures. To address the challenges associated with securing CPSs, two novel approaches are introduced by representing a CPS in terms of a graph structure. The first approach is an automated graph-based information flow model introduced to identify information flow paths in the avionics system and partition them into security domains. This approach is applied to selected aspects of the avionic systems to identify the vulnerabilities in case of a system failure or an attack and provide possible mitigation measures. The second approach is based on graph neural networks (GNN) to classify the graphs into different security domains. Using these two approaches, successful partitioning of the CPS into different security domains is possible in addition to identifying their optimal coverage. These approaches enable designers and engineers to ensure the integrity of the CPS. The engineers and operators can use this process during design-time and in real-time to identify failures or attacks on the system”--Abstract, page iii