Achieving cyber resilient critical infrastructure poses a significant engineering management challenge. Society relies on infrastructure and services that extend beyond the managerial boundaries of a specific organizational entity, yet existing cybersecurity maturity models typically aim to assess a single organization. We offer a systems thinking approach to cyber resilience. Specifically, we relate to critical infrastructure and services in their sectoral system context, reimagining them as a system of systems. We then suggest exploring cyber resilience as a system property, with its expressions relating to the multiple dimensions of operation of the sector and to the different domains of practice. We discuss the dimensions of operation and domains of practice concepts that are embedded into a sectoral cyber resilience maturity model, which is under development. We demonstrate how these concepts frame a set of expressions that is designed to probe the sectoral design space; and propose how they may be further used as design considerations for improving the sector's cyber resilience.

In research, process descriptions provide causal mechanisms and context as a basis for establishing phenomena and enabling reproducibility and generalization, and are therefore considered essential to scientific progress. Process descriptions are a principle form of describing systems. However, process descriptions are typically unstructured and complex, presenting challenges with respect to the assessment of their quality, and eventually to their usability. Inspired by modern systems engineering approaches, we propose a model-based analysis approach to analyze complex process descriptions, and consequently improve the communicability and the applicability of said processes. We apply the approach to real-life case studies, and detail its contribution. Finally, we discuss how using the model-based approach can promote the quality of research-related process descriptions, specifically for supporting evaluation of research publications as well as for describing processes by the researchers themselves.

Radar systems are mainly used for tracking aircraft, missiles, satellites, and watercraft. In many cases, information regarding the objects detected by a radar system is sent to, and used by, a peripheral consuming system, such as a missile system or a graphical user interface used by an operator. Those systems process the data stream and make real-time operational decisions based on the data received. Given this, the reliability and availability of information provided by radar systems have grown in importance. Although the field of cyber security has been continuously evolving, no prior research has focused on anomaly detection in radar systems. In this paper, we present an unsupervised deep-learning-based method for detecting anomalies in radar system data streams; we take into consideration the fact that a data stream created by a radar system is heterogeneous, i.e., it contains both numerical and categorical features with non-linear and complex relationships. We propose a novel technique that learns the correlation between numerical features and an embedding representation of categorical features in an unsupervised manner. The proposed technique, which allows for the detection of the malicious manipulation of critical fields in a data stream, is complemented by a timing-interval anomaly-detection mechanism proposed for the detection of message-dropping attempts. Real radar system data were used to evaluate the proposed method. Our experiments demonstrated the method’s high detection accuracy on a variety of data-stream manipulation attacks (an average detection rate of 88% with a false -alarm rate of 1.59%) and message-dropping attacks (an average detection rate of 92% with a false-alarm rate of 2.2%).