A Design Approach to IoT Endpoint Security for Production Machinery Monitoring

The Internet of Things (IoT) has significant potential in upgrading legacy production machinery with monitoring capabilities to unlock new capabilities and bring economic benefits. However, the introduction of IoT at the shop floor layer exposes it to additional security risks with potentially significant adverse operational impact. This article addresses such fundamental new risks at their root by introducing a novel endpoint security-by-design approach. The approach is implemented on a widely applicable production-machinery-monitoring application by introducing real-time adaptation features for IoT device security through subsystem isolation and a dedicated lightweight authentication protocol. This paper establishes a novel viewpoint for the understanding of IoT endpoint security risks and relevant mitigation strategies and opens a new space of risk-averse designs that enable IoT benefits, while shielding operational integrity in industrial environments

Two architectural threat analysis techniques compared

In an initial attempt to systematize the research field of architectural threat analysis, this paper presents a comparative study of two threat analysis techniques. In particular, the controlled experiment presented here compares two variants of Microsoft’s STRIDE. The two variants differ in the way the analysis is performed. In one case, each component of the software system is considered in isolation and scrutinized for potential security threats. In the other case, the analysis has a wider scope and considers the security threats that might occur in a pair of interacting software components. The study compares the techniques with respect to their effectiveness in finding security threats (benefits) as well as the time that it takes to perform the analysis (cost). We also look into other human aspects which are important for industrial adoption, like, for instance, the perceived difficulty in learning and applying the techniques as well as the overall preference of our experimental participants

The Formal Representation of Cyberthreats for Automated Reasoning

© Springer Nature Switzerland AG 2020. Considering the complexity and dynamic nature of cyberthreats, the automation of data-driven analytics in cyberthreat intelligence is highly desired. However, the terminology of cyberthreat intelligence varies between methods, techniques, and applications, and the corresponding expert knowledge is not codified, making threat data inefficient, and sometimes infeasible, to process by semantic software agents. Therefore, various data models, methods, and knowledge organization systems have been proposed over the years, which facilitate knowledge discovery, data aggregation, intrusion detection, incident response, and comprehensive and automated data analysis. This chapter reviews the most influential and widely deployed cyberthreat classification models, machine-readable taxonomies, and machine-interpretable ontologies that are well-utilized in cyberthreat intelligence applications