Security and privacy (SP)-related challenges constitute a significant barrier to the wider adoption of Internet of Things (IoT)/Industrial IoT (IIoT) devices and the associated novel applications and services. In this context, patterns, which are constructs encoding re-usable solutions to common problems and building blocks to architectures, can be an asset in alleviating said barrier. More specifically, patterns can be used to encode dependencies between SP properties of individual smart objects and corresponding properties of orchestrations (compositions) involving them, facilitating the design of IoT solutions that are secure and privacy-aware by design. Motivated by the above, this work presents a survey and taxonomy of SP patterns towards the creation of a usable pattern collection. The aim is to enable decomposition of higher-level properties to more specific ones, matching them to relevant patterns, while also creating a comprehensive overview of security- and privacy-related properties and sub-properties that are of interest in IoT/IIoT environments. To this end, the identified patterns are organized using a hierarchical taxonomy that allows their classification based on provided property, context, and generality, while also showing the relationships between them. The two high-level properties, Security and Privacy, are decomposed to a first layer of lower-level sub-properties such as confidentiality and anonymity. The lower layers of the taxonomy, then, include implementation-level enablers. The coverage that these patterns offer in terms of the considered properties, data states (data in transit, at rest, and in process), and platform connectivity cases (within the same IoT platform and across different IoT platforms) is also highlighted. Furthermore, pointers to extensions of the pattern collection to include additional patterns and properties, including Dependability and Interoperability, are given. Finally, to showcase the use of the presented pattern collection, a practical application is detailed, involving the pattern-driven composition of IoT/IIoT orchestrations with SP property guarantees.

As robotic systems become more integrated into our daily lives, there is growing concern about cybersecurity. Robots used in areas such as autonomous driving, surveillance, surgery, home assistance, and industrial automation can be vulnerable to cyber-attacks, which could have serious real-world consequences. Modern robotic systems face a unique set of threats due to their evolving characteristics. This paper outlines the SESAME project’s methodology for the automated security analysis of multi-robot systems (MRS) and the production of Executable Digital Dependability Identities (EDDIs). Addressing security challenges in MRS involves overcoming complex factors such as increased connectivity, human–robot interactions, and a lack of risk awareness. The proposed methodology encompasses a detailed process, starting from system description and vulnerability identification and moving to the generation of attack trees and security EDDIs. The SESAME security methodology leverages structured repositories like Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), and Common Attack Pattern Enumeration and Classification (CAPEC) to identify potential vulnerabilities and associated attacks. The introduction of Template Attack Trees facilitates modeling potential attacks, helping security experts develop effective mitigation strategies. This approach not only identifies, but also connects, specific vulnerabilities to possible exploits, thereby generating comprehensive security assessments. By merging safety and security assessments, this methodology ensures the overall dependability of MRS, providing a robust framework to mitigate cyber–physical threats.

Modern computing paradigms (i.e., cloud, edge, Internet of Things) and ubiquitous connectivity have brought the notion of pervasive computing to an unforeseeable level, which boosts service-oriented architectures and microservices patterns to create digital services with data-centric models. However, the resulting agility in service creation and management has not been followed by a similar evolution in cybersecurity patterns, which still largely rest on more conventional device- and infrastructure-centric models.In this Chapter, we describe the implementation of the GUARD Platform, which represents the core element of a modern cybersecurity framework for building detection and analytics services for complex digital service chains. We briefly review the logical components and how they address scientific and technological challenges behind the limitations of existing cybersecurity tools. We also provide validation and performance analysis that show the feasibility and efficiency of our implementation.

The adoption of Internet of Things (IoT) devices, applications and services gradually transform our everyday lives. In parallel, the transition from linear to circular economic (CE) models provide an even more fertile ground for novel types of services, and the update and enrichment of legacy ones. To fully realize the potential of the interplay between IoT and CE, the design-time definition of IoT orchestrations with proven circularity properties, and the run-time management of these orchestrations based on said properties, is of paramount importance. Nevertheless, the circularity requirements and associated properties are not only difficult to achieve at the IoT orchestration design and deployment initialization phases, but also hard to prove and maintain at run-time. Motivated by this, this paper presents the CIRCE framework for circular and trustworthy by-design IoT orchestrations. The CIRCE approach leverages concepts from pattern-driven engineering, whereby patterns are used to encode proven dependencies between the Location, Condition, and Availability (LCA) properties of individual smart objects and corresponding properties of orchestrations (compositions) involving them. These are augmented by patterns encoding trustworthiness-related properties, namely Connectivity, Security, Privacy, Dependability, and Interoperability (CSPDI). Thereby, these patterns are used to generate IoT orchestrations with proven LCA and CSPDI properties, as needed, at design time. At runtime, these properties are monitored in real-time, leveraging reasoning engines deployed across system layers, triggering adaptations to return the deployed orchestration to the desired LCA and CSPDI states, when required. Details are provided on the above novel combination of IoT, CE and pattern-based engineering, along with a proposed architecture and implementation approach. Furthermore, an assessment of a proof-of-concept implementation is provided, validating the feasibility of the proposed approach.