The Internet of Things (IoT) is rapidly changing the way in which we engage with technology on a daily basis. The IoT paradigm enables low-resource devices to intercommunicate in a fully flexible and pervasive manner, and the data from these devices is used for decision-making in critical applications such as; traffic infrastructure, health-care and home security, to name but a few. Due to the scarce resources available in these IoT devices, being able to quantify the reliability of them is a critical function. This report presents a detailed evolution of the area of reliability measurement, followed by an in-depth review of the state-of-the-art for quantification of reliability in the IoT, revealing the many challenges associated with this task. From this in-depth review, a set of key research directions for IoT reliability is determined. Despite the critical nature of the research area, at this current moment, this study is the first detailed review available in the area of assessing IoT reliability.

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IoT reliability: a review leading to 5 key research directions

The factories of the future require efficient interconnection of their physical machines into the cyber space to cope with the emerging need of an increased uptime of machines, higher performance rates, an improved level of productivity and a collective collaboration along the supply chain. With the rapid growth of the Internet of Things (IoT), and its application in industrial areas, the so called Industrial Internet of Things (IIoT)/Industry 4.0 emerged. However, further to the rapid growth of IoT/IIoT systems, cyber attacks are an emerging threat and simple manual security testing can often not cope with the scale of large IoT/IIoT networks. In this paper, we suggest to extract metadata from commonly used diagrams and models in a typical software development process, to automate the process of threat modelling, security analysis and penetration testing, without detailed prior security knowledge. In that context, we present requirements and recommendations for metadata in IoT/IIoT models that are needed as necessary input parameters of security assurance tools.

Requirements and Recommendations for IoT/IIoT Models to automate Security Assurance through Threat Modelling, Security Analysis and Penetration Testing

Continuous risk monitoring is considered in the context of cybersecurity management for the Industrial Internet-of-Thing. Cyber-risk management best practice is for security controls to be deployed and configured in order to bring down risk exposure to an acceptable level. However, threats and known vulnerabilities are subject to change, and estimates of risk are subject to many uncertainties, so it is important to review risk assessments and update controls when required. Risks are typically reviewed periodically (e.g. once per month), but the accelerating pace of change means that this approach is not sustainable, and there is a requirement for continuous monitoring of cybersecurity risks. The method described in this paper aims to alert security staff of significant changes or trends in estimated risk exposure to facilitate rational and timely decisions. Additionally, it helps predict the success and impact of a nascent security breach allowing better prioritisation of threats and selection of appropriate responses. The method is illustrated using a scenario based on environmental control in a data centre.

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Cyber-Risks in the Industrial Internet of Things (IIoT): Towards a Method for Continuous Assessment.

The paper considers approaches to the integration of heterogeneous data sources and correlations of disparate security events for protection against cyber-physical attacks. The architecture of the proposed integrated security system, milestones and methods of data correlation, as well as examples of the application of the system are suggested.

Event correlation in the integrated cyber-physical security system

Ensuring security for modern IoT systems requires the use of complex methods to analyze their software. One of the most in-demand methods that has repeatedly been proven to be effective is static analysis. However, the progressive complication of the connections in IoT systems, the increase in their scale, and the heterogeneity of elements requires the automation and intellectualization of manual experts’ work. A hypothesis to this end is posed that assumes the applicability of machine-learning solutions for IoT system static analysis. A scheme of this research, which is aimed at confirming the hypothesis and reflecting the ontology of the study, is given. The main contributions to the work are as follows: systematization of static analysis stages for IoT systems and decisions of machine-learning problems in the form of formalized models; review of the entire subject area publications with analysis of the results; confirmation of the machine-learning instrumentaries applicability for each static analysis stage; and the proposal of an intelligent framework concept for the static analysis of IoT systems. The novelty of the results obtained is a consideration of the entire process of static analysis (from the beginning of IoT system research to the final delivery of the results), consideration of each stage from the entirely given set of machine-learning solutions perspective, as well as formalization of the stages and solutions in the form of “Form and Content” data transformations.

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Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning Approaches

The increasing urgency and expansion of information systems implementing the Internet of Things (IoT) concept determine the importance of the investigation in the field of protection mechanisms against a wide range of information security threats. The increased complexity of such investigation is determined by a low structuring and formalization of expert knowledge on IoT systems. The paper encompasses an approach to elicitation and use of expert knowledge on detection of anomalies in data as well as their usage as an input for automated means aimed at monitoring security components of IoT.

S. B. Nogin

Papers

Detection of anomalies in data for monitoring of security components in the Internet of Things