Methodology for Designing Decision Support Systems for Visualising and Mitigating Supply Chain Cyber Risk from IoT Technologies

This paper proposes a methodology for designing decision support systems for visualising and mitigating the Internet of Things cyber risks. Digital technologies present new cyber risk in the supply chain which are often not visible to companies participating in the supply chains. This study investigates how the Internet of Things cyber risks can be visualised and mitigated in the process of designing business and supply chain strategies. The emerging DSS methodology present new findings on how digital technologies affect business and supply chain systems. Through epistemological analysis, the article derives with a decision support system for visualising supply chain cyber risk from Internet of Things digital technologies. Such methods do not exist at present and this represents the first attempt to devise a decision support system that would enable practitioners to develop a step by step process for visualising, assessing and mitigating the emerging cyber risk from IoT technologies on shared infrastructure in legacy supply chain systems

Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems, cyber risk at the edge

The Internet of Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state of the art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analysis is presented as the current and a target risk state for IoT systems, followed by conclusions and recommendations on a transformation roadmap, describing how IoT systems can achieve the target state with a new goal-oriented dependency model. By target state, we refer to the cyber security target that matches the generic security requirements of an organisation. The research paper studies and adapts four alternatives for IoT risk assessment and identifies the goal-oriented dependency modelling as a dominant approach among the risk assessment models studied. The new goal-oriented dependency model in this article enables the assessment of uncontrollable risk states in complex IoT systems and can be used for a quantitative self-assessment of IoT cyber risk posture

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

Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks

Definition of Internet of Things (IoT) Cyber Risk – Discussion on a Transformation Roadmap for Standardization of Regulations, Risk Maturity, Strategy Design and Impact Assessment

Definition of Internet of Things (IoT) Cyber Risk – Discussion on a Transformation Roadmap for Standardization of Regulations, Risk Maturity, Strategy Design and Impact Assessmen

Definition of Internet of Things (IoT) Cyber Risk – Discussion on a Transformation Roadmap for Standardization of Regulations, Risk Maturity, Strategy Design and Impact Assessment

Definition of Internet of Things (IoT) Cyber Risk – Discussion on a Transformation Roadmap for Standardization of Regulations, Risk Maturity, Strategy Design and Impact Assessmen

Future developments in cyber risk assessment for the internet of things

This article is focused on the economic impact assessment of Internet of Things (IoT) and its associated cyber risks vectors and vertices – a reinterpretation of IoT verticals. We adapt to IoT both the Cyber Value at Risk model, a well-established model for measuring the maximum possible loss over a given time period, and the MicroMort model, a widely used model for predicting uncertainty through units of mortality risk. The resulting new IoT MicroMort for calculating IoT risk is tested and validated with real data from the BullGuard's IoT Scanner (over 310,000 scans) and the Garner report on IoT connected devices. Two calculations are developed, the current state of IoT cyber risk and the future forecasts of IoT cyber risk. Our work therefore advances the efforts of integrating cyber risk impact assessments and offer a better understanding of economic impact assessment for IoT cyber risk

Implementing a design thinking approach to de-risk the digitalisation of manufacturing SMEs

Industry 4.0 (I4.0) has proposed a significant shift in the way companies approach manufacturing. However, this new paradigm is not without faults. The integration of processes and equipment (‘digitalisation’) can be prohibitively expensive or too technically complex for small-to-medium enterprises (SMEs) with limited resources and technical expertise. Another barrier to digitalisation lies in the ambiguity of not knowing what precise practices to adopt to improve productivity. Although these challenges have been identified in the literature, there is still little evidence on how to tackle them. Thus, we explore how design thinking can help overcome these challenges, given that it has been used in many organisations and disciplines to deal with complex and ambiguous problems. We do so by investigating the research question ‘How can designers and design thinking processes assist manufacturing SMEs’ digitalisation?’ We address this research question by presenting a case study of a university–industry collaboration where the authors utilised a design-thinking process to select and implement technologies to capture, process and analyse data for an Australian medical device manufacturer. By reflecting on the case study, we identified the user-centeredness of design thinking as crucial in selecting technologies for implementation that prioritised usability and brought value to all stakeholders. Furthermore, iterative prototyping was critical to scale up the required expertise and deliver a successful sustainable solution without investing vast resources. Our work suggests that designers and design thinking have the potential to help de-risk digitalisation. Finally, we suggest a framework that may assist in guiding other SMEs approaching digitalisation and provide a starting point for further design thinking research in this area

Future developments in standardisation of cyber risk in the Internet of Things (IoT)

In this research article, we explore the use of a design process for adapting existing cyber risk assessment standards to allow the calculation of economic impact from IoT cyber risk. The paper presents a new model that includes a design process with new risk assessment vectors, specific for IoT cyber risk. To design new risk assessment vectors for IoT, the study applied a range of methodologies, including literature review, empirical study and comparative study, followed by theoretical analysis and grounded theory. An epistemological framework emerges from applying the constructivist grounded theory methodology to draw on knowledge from existing cyber risk frameworks, models and methodologies. This framework presents the current gaps in cyber risk standards and policies, and defines the design principles of future cyber risk impact assessment. The core contribution of the article therefore, being the presentation of a new model for impact assessment of IoT cyber risk

Assessing Security Risk and Requirements for Systems of Systems

A System of Systems (SoS) is a term used to describe independent systems converging for a purpose that could only be carried out through this interdependent collaboration. Many examples of SoSs exist, but the term has become a source of confusion across domains. Moreover, there are few illustrative SoS examples demonstrating their initial classification and structure. While there are many approaches for engineering of systems, less exist for SoS engineering. More specifically, there is a research gap towards approaches addressing SoS security risk assessment for engineering and operational needs, with a need for tool-support to assist modelling and visualising security risk and requirements in an interconnected SoS. From this, security requirements can provide a systematic means to identify constraints and related risks of the SoS, mitigated by human-user and system requirements. This work investigates specific challenges and current approaches for SoS security and risk, and aims to identify the alignment of SoS factors and concepts suitable for eliciting, analysing, validating risks with use of a tool-support for assessing security risk in the SoS context

Optimising a defence-aware threat modelling diagram incorporating a defence-in-depth approach for the internet-of-things

Modern technology has proliferated into just about every aspect of life while improving the quality of life. For instance, IoT technology has significantly improved over traditional systems, providing easy life, time-saving, financial saving, and security aspects. However, security weaknesses associated with IoT technology can pose a significant threat to the human factor. For instance, smart doorbells can make household life easier, save time, save money, and provide surveillance security. Nevertheless, the security weaknesses in smart doorbells could be exposed to a criminal and pose a danger to the life and money of the household. In addition, IoT technology is constantly advancing and expanding and rapidly becoming ubiquitous in modern society. In that case, increased usage and technological advancement create security weaknesses that attract cybercriminals looking to satisfy their agendas. Perfect security solutions do not exist in the real world because modern systems are continuously improving, and intruders frequently attempt various techniques to discover security flaws and bypass existing security control in modern systems. In that case, threat modelling is a great starting point in understanding the threat landscape of the system and its weaknesses. Therefore, the threat modelling field in computer science was significantly improved by implementing various frameworks to identify threats and address them to mitigate them. However, most mature threat modelling frameworks are implemented for traditional IT systems that only consider software-related weaknesses and do not address the physical attributes. This approach may not be practical for IoT technology because it inherits software and physical security weaknesses. However, scholars employed mature threat modelling frameworks such as STRIDE on IoT technology because mature frameworks still include security concepts that are significant for modern technology. Therefore, mature frameworks cannot be ignored but are not efficient in addressing the threat associated with modern systems. As a solution, this research study aims to extract the significant security concept of matured threat modelling frameworks and utilise them to implement robust IoT threat modelling frameworks. This study selected fifteen threat modelling frameworks from among researchers and the defence-in-depth security concept to extract threat modelling techniques. Subsequently, this research study conducted three independent reviews to discover valuable threat modelling concepts and their usefulness for IoT technology. The first study deduced that integration of threat modelling approach software-centric, asset-centric, attacker-centric and data-centric with defence-in-depth is valuable and delivers distinct benefits. As a result, PASTA and TRIKE demonstrated four threat modelling approaches based on a classification scheme. The second study deduced the features of a threat modelling framework that achieves a high satisfaction level toward defence-in-depth security architecture. Under evaluation criteria, the PASTA framework scored the highest satisfaction value. Finally, the third study deduced IoT systematic threat modelling techniques based on recent research studies. As a result, the STRIDE framework was identified as the most popular framework, and other frameworks demonstrated effective capabilities valuable to IoT technology. Respectively, this study introduced Defence-aware Threat Modelling (DATM), an IoT threat modelling framework based on the findings of threat modelling and defence-in-depth security concepts. The steps involved with the DATM framework are further described with figures for better understatement. Subsequently, a smart doorbell case study is considered for threat modelling using the DATM framework for validation. Furthermore, the outcome of the case study was further assessed with the findings of three research studies and validated the DATM framework. Moreover, the outcome of this thesis is helpful for researchers who want to conduct threat modelling in IoT environments and design a novel threat modelling framework suitable for IoT technology