A Review of Critical Infrastructure Protection Approaches: Improving Security through Responsiveness to the Dynamic Modelling Landscape

TL;DR

This paper systematically reviews critical infrastructure protection approaches, highlighting the predominance of empirical and agent-based models, and discusses their suitability amid emerging IoT security challenges.

Contribution

It provides a comprehensive comparison of existing CIP approaches, emphasizing their applicability and limitations in the context of modern interconnected infrastructures.

Findings

Empirical-based modelling is the most widely used in CIP.

Agent-based and system dynamics models are commonly applied.

No single approach fits all sectors or scenarios.

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.