Industry 4.0 (I4.0) is an emerging concept describing the business setting application of a broad set of digitalisation technologies, connectivity, and automation. The most common critical infrastructure (CI) uses Industrial Control Systems (ICS) for operation and supervisory control. However, the Supervisory Control and Data Acquisition (SCADA) and Internet of things (IoT) systems are examples of ICSs applications. These systems, like any other systems exposed to many security risks and are vulnerable to many threats. This is mainly due to the lack of objective standards and proactive security countermeasures that companies unintentionally neglected in the early stages of designing these systems. It is also due to the absence of managerial and technical skills necessary to implement them. Therefore, identifying and preventing potential security threats against CIs is the focus of this paper. A novel security approach concept that can predict cybersecurity threats based on the CI nature and take into consideration the attack motivations accordingly has been delivered in this paper. The proposed concept of this approach will also facilitate the detection of potential attack types and the required countermeasures in particular infrastructures.

PurposeThis study aims to propose a cybersecurity framework that prioritizes sustainability in the manufacturing sector by identifying necessary resources and capabilities for effective cybersecurity management. The proposed framework aims to enhance resource protection and safeguard data confidentiality, integrity and accessibility, provide proactive steps for predicting cyber threats and highlight the importance of educating employees at all levels of the organization.Design/methodology/approachA thorough review of existing literature and analysis was conducted to develop the proposed cybersecurity framework. Several frameworks, including the NIST cybersecurity framework, were reviewed to identify the necessary skills and resources required to combat cyber threats and keep businesses sustainable.FindingsThe proposed framework includes proactive steps, such as predicting cyber threats, and emphasizes the importance of educating employees and raising awareness at all levels of the organization. Resilience is also emphasized, which refers to an organization's ability to recover and continue operations following a cyberattack. Implementing this framework may require a significant budget and time investment, and small organizations may face limitations in applying all aspects of the framework.Originality/valueThis study proposes a cybersecurity framework that prioritizes sustainability in the manufacturing sector, which provides added protection for organizations. The framework's key functions can be adopted partially or fully, making it suitable for organizations of varying sizes. Future research can focus on addressing the framework's limitations and shortcomings to further reduce cyber risks for sustainable manufacturing, establishing the scale of an industry based on its economy and extending the framework to non-manufacturing businesses.

In Industry 4.0, manufacturing and critical systems require high levels of flexibility and resilience for dynamic outcomes. Industrial Control Systems (ICS), specifically Supervisory Control and Data Acquisition (SCADA) systems, are commonly used for operation and control of Critical Infrastructure (CI). However, due to the lack of security controls, standards, and proactive security measures in the design of these systems, they have security risks and vulnerabilities. Therefore, efficient and effective security solutions are needed to secure the conjunction between CI and I4.0 applications. This paper predicts potential cyberattacks and threats against CI systems by considering attacker motivations and using machine learning models. The approach presents a novel cybersecurity prediction technique that forecasts potential attack methods, depending on specific CI and attacker motivations. The proposed model’s accuracy in terms of False Positive Rate (FPR) reached 66% with the trained and test datasets. This proactive approach predicts potential attack methods based on specific CI and attacker motivations, and doubling the trained data sets will improve the accuracy of the proposed model in the future.

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