In the emerging industrial Internet of Things (IIoT) era, machine-to-machine (M2M) communication technology is considered as a key underlying technology for building IIoT environments, where devices (e.g., sensors, actuators, and gateways) are enabled to exchange information with each other in an autonomous way without human intervention. However, most of the existing M2M protocols that can be also used in the IIoT domain provide security mechanisms based on asymmetric cryptography resulting in high computational cost. As a consequence, the resource-constrained IoT devices are not able to support them appropriately and thus, many security issues arise for the IIoT environment. Therefore, lightweight security mechanisms are required for M2M communications in IIoT in order to reach its full potential. As a step toward this direction, in this paper, we propose a lightweight authentication mechanism, based only on hash and XOR operations, for M2M communications in IIoT environment. The proposed mechanism is characterized by low computational cost, communication, and storage overhead, while achieving mutual authentication, session key agreement, device's identity confidentiality, and resistance against the following attacks: replay attack, man-in-the-middle attack, impersonation attack, and modification attack.

The growing demand for interoperability between system components within a connected car has led to new security challenges in automotive development. The existing components, based on established technology, are often being combined to form such a connected car. For such established technologies, individual, often sector specific threat and vulnerability catalogs exist. The aim of this paper is to identify blocks of established technologies in a connected car and to consolidate the corresponding threat and vulnerability catalogs relevant for the individual constituent components. These findings are used to estimate the impact on specific system components and subsystems to identify the most crucial components and threats.

Cyber-physical production systems are engineered systems that are built from, and depend upon, the seamless integration of computational algorithms and physical components. In order to make these systems interoperable with each other for addressing Industry 4.0 applications a number of service-oriented architecture frameworks are developed. Such frameworks are composed by a number of services, which are inherently dynamic by nature and thus imply the need for self-adaptation. In this paper we propose generic autonomic management as a service and show how it can be integrated in the Arrowhead framework. We propose generic and reusable interfaces for each phase of the autonomic control loop in order to increase the usability of the service for other frameworks and application systems, while reducing the software engineering effort. To show the utility of our approach in the Arrowhead framework we use a climate control application as a representative example.

A high level of information security in critical infrastructure IT systems and services has to be preserved when migrating their IT services to the cloud. Often various legislative and security constraints have to be met in line with best practice guidelines and international standards to perform the migration. To support the critical infrastructure providers in migrating their services to the cloud we are developing a process based migration guideline for critical infrastructure providers focusing on information security. First of all we investigate, via questionnaires, how the importance of individual security topics covered in such guidelines differentiates between industry stakeholders and critical infrastructure providers. This supports the selection of relevant security topics and the considered guidelines and standards, which we survey in search for common relevant security topics. Subsequently we present the analysis of the above-mentioned security requirements and how they affect a here developed taxonomy for a process-based security guideline. Furthermore we present potential service migration use cases and how our methodology would affect the migration of secure critical infrastructure services.

Industrial automation systems are advancing rapidly and a wide range of standards, communication protocols and platforms supporting the integration of devices are introduced. It is therefore necessary to design and build appropriate tools and frameworks that allow the integration of devices with multiple systems and services. In this work we present the Arrow-head Framework, used to enable collaborative IoT automation and introduce two support core systems, SystemRegistry and DeviceRegistry, which are needed to create a chain of trust from a hardware device to a software system and its associated services. Furthermore, we propose an on-boarding procedure of a new device interacting with the Arrowhead local cloud. This ensures that only valid and authorized devices can host software systems within an Arrowhead local cloud.

The fast growing number of cloud based Infrastructure-as-a-Service instances raises the question, how the operations security depending on the underlying cloud computing infrastructure can be sustained and guaranteed. Security standards provide guidelines for information security controls applicable to the provision and use of the cloud services. The objectives of operations security are to support planning and sustaining of day-to-day processes that are critical with respect to security of information environments. In this work we provide a detailed analysis of ISO 27017 standard regarding security controls and investigate how well popular cloud platforms can cater for them. The resulting gap of support for individual security controls is furthermore compared with outcomes of recent cloud security research projects. Hence the contribution is twofold, first we identify a set of topics that still require research and development and secondly, as a practical output, we provide a comparison of popular industrial and open-source platforms focusing on private cloud environments, which are important for Industry 4.0 use cases.

Manufacturing enterprises are constantly exploring new ways to improve their own production processes to address the increasing demand of customized production. However, such enterprises show a low degree of flexibility, which mainly results from the need to configure new production equipment at design and run time. In this paper we propose self-adaptation as an approach to improve data transmission flexibility in Industry 4.0 environments. We implement an autonomic manager using a generic autonomic management framework, which applies the most appropriate data transmission configuration based on security and business process related requirements, such as performance. The experimental evaluation is carried out in a MQTT infrastructure and the results show that using self-adaptation can significantly improve the trade-off between security and performance. We then propose to integrate anomaly detection methods as a solution to support self-adaptation by monitoring and learning the normal behavior of an industrial system and show how this can be used by the generic autonomic management framework.

An analysis of new technologies can yield insight into the way these technologies will be used. Inevitably, new technologies and their uses are likely to result in new security issues regarding threats, vulnerabilities and attack vectors. In this paper, we investigate and analyse technological and security trends and their potential to become future threats by systematically examining industry reports on existing technologies. Using a cloud computing use case we identify potential resilience metrics that can shed light on the security properties of the system.