Nowadays, the industrial sector is being challenged by several cybersecurity concerns. Direct attacks by malicious persons and (or) software form part of the severe jeopardies of industrial control systems (ICSs). These affect products/production qualities, brand reputations, sales revenues, and aggravate the risks to health and safety of human lives. These have been enabled due to progressive adoption of technology trends like Industry 4.0, BYOD, mobile computing, and Internet-of-Things (IoT), in the quest for improved relevance and value of production decisions, minimised operational overheads, optimum resource utilisation, markets globalisation, etc. However, several security vulnerabilities and risks have also emerged, and are increasingly being exploited in the industrial sector especially manufacturing. To manage this phenomenon, refined and holistic (combining people, process, and technology perspectives) security strategies and solutions are required to enhance security in ICS. This paper offers an insightful review of possible solution path beginning with the understanding of ICS security trends relative to cyber threats, vulnerabilities, attacks and patterns, agents, risks, and the impacts of all these on the industrial environment and entities that depend on it. Such episteme can improve security awareness, proficiency for respective stakeholders, and advance the development of appropriate security mechanisms, and adoption of recommendations.

This article conducts a literature review of current and future challenges in the use of artificial intelligence (AI) in cyber physical systems. The literature review is focused on identifying a conceptual framework for increasing resilience with AI through automation supporting both, a technical and human level. The methodology applied resembled a literature review and taxonomic analysis of complex internet of things (IoT) interconnected and coupled cyber physical systems. There is an increased attention on propositions on models, infrastructures and frameworks of IoT in both academic and technical papers. These reports and publications frequently represent a juxtaposition of other related systems and technologies (e.g. Industrial Internet of Things, Cyber Physical Systems, Industry 4.0 etc.). We review academic and industry papers published between 2010 and 2020. The results determine a new hierarchical cascading conceptual framework for analysing the evolution of AI decision-making in cyber physical systems. We argue that such evolution is inevitable and autonomous because of the increased integration of connected devices (IoT) in cyber physical systems. To support this argument, taxonomic methodology is adapted and applied for transparency and justifications of concepts selection decisions through building summary maps that are applied for designing the hierarchical cascading conceptual framework.

This article investigates cybersecurity (and risk) in the context of ‘technological singularity’ from artificial intelligence. The investigation constructs multiple risk forecasts that are synthesised in a new framework for counteracting risks from artificial intelligence (AI) itself. In other words, the research in this article is not just concerned with securing a system, but also analysing how the system responds when (internal and external) failure(s) and compromise(s) occur. This is an important methodological principle because not all systems can be secured, and totally securing a system is not feasible. Thus, we need to construct algorithms that will enable systems to continue operating even when parts of the system have been compromised. Furthermore, the article forecasts emerging cyber-risks from the integration of AI in cybersecurity. Based on the forecasts, the article is concentrated on creating synergies between the existing literature, the data sources identified in the survey, and forecasts. The forecasts are used to increase the feasibility of the overall research and enable the development of novel methodologies that uses AI to defend from cyber risks. The methodology is focused on addressing the risk of AI attacks, as well as to forecast the value of AI in defence and in the prevention of AI rogue devices acting independently.

This paper presents a mapping framework for design factors and an implementation process for building credible Industrial Control Systems (ICS) security testbeds. The security and resilience of ICSs has become a critical concern to operators and governments following widely publicised cyber security events. The inability to apply conventional Information Technology security practice to ICSs further compounds challenges in adequately securing critical systems. To overcome these challenges, and do so without impacting live environments, testbeds are widely used for the exploration, development, and evaluation of security controls. However, how a testbed is designed and its attributes, can directly impact not only its viability but also its credibility. Combining systematic and thematic analysis, and the mapping of identified ICS security testbed design attributes, we propose a novel relationship map of credibility-supporting design factors (and their associated attributes) and a process implementation flow structure for ICS security testbeds. The framework and implementation process highlight the significance of demonstrating some design factors such as user/experimenter expertise, clearly defined testbed design objectives, simulation implementation approach, covered architectural components, core structural and functional characteristics covered, and evaluations to enhance confidence, trustworthiness and acceptance of ICS security testbeds as credible. These can streamline testbed requirement definition, improve design consistency and quality while reducing implementation costs.