Towards a Security, Privacy, Dependability, Interoperability Framework for the Internet of Things

A popular application of ambient intelligence systems constitutes of assisting living services on smart buildings. As intelligence is imported in embedded equipment, the system becomes able to provide smart services (e.g. control lights, airconditioning, provide energy management services etc.). IoT is the main enabler of such environments. However, the interconnection of these cyber-physical systems and the processing of personal data raise serious security and privacy issues. In this paper we present a framework that can guarantee Security, Privacy, Dependability and Interoperability (SPDI) in IoT. Taking advantage of the underlying IoT deployment, the proposed framework not only implements the requested smart functionality but also provide modelling and administration that can guarantee those SPDI properties. Moreover, we provide an application example of the framework in a smart building scenario

Towards a Collection of Security and Privacy Patterns

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

CIRCE: Architectural Patterns for Circular and Trustworthy By-Design IoT Orchestrations

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

Security Risk Management for the Internet of Things

In recent years, the rising complexity of Internet of Things (IoT) systems has increased their potential vulnerabilities and introduced new cybersecurity challenges. In this context, state of the art methods and technologies for security risk assessment have prominent limitations when it comes to large scale, cyber-physical and interconnected IoT systems. Risk assessments for modern IoT systems must be frequent, dynamic and driven by knowledge about both cyber and physical assets. Furthermore, they should be more proactive, more automated, and able to leverage information shared across IoT value chains. This book introduces a set of novel risk assessment techniques and their role in the IoT Security risk management process. Specifically, it presents architectures and platforms for end-to-end security, including their implementation based on the edge/fog computing paradigm. It also highlights machine learning techniques that boost the automation and proactiveness of IoT security risk assessments. Furthermore, blockchain solutions for open and transparent sharing of IoT security information across the supply chain are introduced. Frameworks for privacy awareness, along with technical measures that enable privacy risk assessment and boost GDPR compliance are also presented. Likewise, the book illustrates novel solutions for security certification of IoT systems, along with techniques for IoT security interoperability. In the coming years, IoT security will be a challenging, yet very exciting journey for IoT stakeholders, including security experts, consultants, security research organizations and IoT solution providers. The book provides knowledge and insights about where we stand on this journey. It also attempts to develop a vision for the future and to help readers start their IoT Security efforts on the right foot

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas

A pattern-based framework for the design of secure and dependable SDN/NFV-enabled networks

As the world becomes an interconnected network where objects and humans interact, cyber and physical networks appear to play an important role in smart ecosystems due to their increasing use on critical infrastructure and smart cities. Software Defined Networking (SDN) and Network Function Virtualisation (NFV) are a promising combination for programmable connectivity, rapid service provisioning and service chaining as they offer the necessary end-to-end optimisations. However, with the actual exponential growth of connected devices, future networks, such as SDN and NFV, require open architectures, facilitated by standards and a strong ecosystem.In this thesis, a model-based approach is proposed to support the design and verification of secure and dependable SDN/NFV-enabled networks. The model is based on the development of a pattern-based approach to design executable patterns as solutions for reusable designs and interactions of objects, encoded in a rule based reasoning system, able to guarantee security and dependability (S&D) properties in SDN/NFV enabled networks. To execute S&D patterns, a pattern based framework is implemented for the insertion of patterns at design and at runtime level. The developed pattern framework highlights also the benefit of leveraging the flexibility of SDN/NFV-enabled networks to deploy enhanced reactive security mechanisms for the protection of the industrial network via the use of service function chaining (SFC). To prove the importance of this approach and the functionality of the pattern framework, different pattern instances are implemented to guarantee S&D in network infrastructures. The developed design patterns are able to design network topologies, guarantee network properties and offer security service provisioning and chaining. Finally, in order to evaluate the developed patterns in the pattern framework, three different use cases are described, where a number of usage scenarios are deployed and evaluated experimentally

New Waves of IoT Technologies Research – Transcending Intelligence and Senses at the Edge to Create Multi Experience Environments

The next wave of Internet of Things (IoT) and Industrial Internet of Things (IIoT) brings new technological developments that incorporate radical advances in Artificial Intelligence (AI), edge computing processing, new sensing capabilities, more security protection and autonomous functions accelerating progress towards the ability for IoT systems to self-develop, self-maintain and self-optimise. The emergence of hyper autonomous IoT applications with enhanced sensing, distributed intelligence, edge processing and connectivity, combined with human augmentation, has the potential to power the transformation and optimisation of industrial sectors and to change the innovation landscape. This chapter is reviewing the most recent advances in the next wave of the IoT by looking not only at the technology enabling the IoT but also at the platforms and smart data aspects that will bring intelligence, sustainability, dependability, autonomy, and will support human-centric solutions.acceptedVersio

Machine Learning Threatens 5G Security

Machine learning (ML) is expected to solve many challenges in the fifth generation (5G) of mobile networks. However, ML will also open the network to several serious cybersecurity vulnerabilities. Most of the learning in ML happens through data gathered from the environment. Un-scrutinized data will have serious consequences on machines absorbing the data to produce actionable intelligence for the network. Scrutinizing the data, on the other hand, opens privacy challenges. Unfortunately, most of the ML systems are borrowed from other disciplines that provide excellent results in small closed environments. The resulting deployment of such ML systems in 5G can inadvertently open the network to serious security challenges such as unfair use of resources, denial of service, as well as leakage of private and confidential information. Therefore, in this article we dig into the weaknesses of the most prominent ML systems that are currently vigorously researched for deployment in 5G. We further classify and survey solutions for avoiding such pitfalls of ML in 5G systems