A review of critical infrastructure protection approaches: improving security through responsiveness to the dynamic modelling landscape

Abstract

As new technologies such as the Internet of Things (IoT) are integrated into Critical National Infrastructures (CNI), new cybersecurity threats emerge that require specific security solutions. Approaches used for analysis include the modelling and simulation of critical infrastructure systems using attributes, functionalities, operations, and behaviours to support various security analysis viewpoints, recognising and appropriately managing associated security risks. With several critical infrastructure protection approaches available, the question of how to effectively model the complex behaviour of interconnected CNI elements and to configure their protection as a system-of-systems remains a challenge. Using a systematic review approach, existing critical infrastructure protection approaches (tools and techniques) are examined to determine their suitability given trends like IoT, and effective security modelling and analysis issues. It is found that empirical-based, agent-based, system dynamics-based, and network-based modelling are more commonly applied than economic-based and equation-based techniques, and empirical-based modelling is the most widely used. The energy and transportation critical infrastructure sectors reflect the most responsive sectors, and no one Critical Infrastructure Protection (CIP) approach -tool, technique, methodology or framework -provides a 'fit-for-all' capacity for all-round attribute modelling and simulation of security risks. Typically, deciding factors for CIP choices to adopt are often dominated by trade-offs between 'complexity of use' and 'popularity of approach', as well as between 'specificity' and 'generality' of application in sectors. Improved security modelling is feasible via; appropriate tweaking of CIP approaches to include a wider scope of security risk management, functional responsiveness to interdependency, resilience and policy formulation requirements, and collaborative information sharing between public and private sectors.

Enabling Secure Data Exchange through the IOTA Tangle for IoT Constrained Devices

“…Security threats and malwares are affecting the IoT as well as Industrial-IoT (IIoT) devices. The need for cybersecurity is widespread due to increasing attacks surface and adversary capabilities [ 33 , 34 ]. Therefore additional hardware peripherals are often employed to provide security functionalities to custom solutions.…”

Cyber risk at the edge: current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains

“…Apart from recovery planning, other challenges found in literature for SME's integration in Industry 4.0 supply chains are: a) robustness, safety, and security (Akinrolabu et al 2019;Brass et al 2018;Brass et al 2019;Hahn et al 2013;Nicolescu et al 2018a;Zhu et al 2011); b) control and hybrid systems (Agyepong et al 2019;Leitão et al 2016;Nurse et al 2018;Shi et al 2011); c) computational abstractions (Ani et al 2019;Madakam et al 2015;Radanliev et al 2018b;Rajkumar et al 2010;Wahlster et al 2013); d) real-time embedded systems abstractions (Ghirardello et al 2018;Kang et al 2012;Leitão et al 2016;Marwedel and Engel 2016;PETRAS 2020;Shi et al 2011;Tan et al 2008); e) model-based development (Bhave et al 2011;Jensen et al 2011;Rajkumar et al 2010;Shi et al 2011;Taylor et al 2018;Wahlster et al 2013); and f) education and training (Faller and Feldmüller 2015;Nicolescu et al 2018b;Petar Radanliev et al 2020;Rajkumar et al 2010;Wahlster et al 2013).…”

IoT in Water Quality Monitoring—Are We Really Here?

“…The Internet of things (IoT) brings an exciting new dimension to the world of field research, where researchers are able to access their data and insights in real time, wherever they are. This means that physical and chemical measurements can be made on-site or in the lab with a fraction of the effort that it would take if you were to rely on manual data collection [ 5 , 9 , 14 ].…”

“…It also means that you can deploy a network of sensors across a region of interest and have them all communicate with each other to provide a comprehensive picture of what is happening in that area. This is particularly valuable for water quality monitoring, where changes in conditions can have serious consequences downstream [ 5 , 9 , 14 ].…”