Biblio

Since the provision of digital services in our days (e.g. container management, transport of COVID vaccinations or LNG) in most economic sectors (e.g. maritime, health, energy) involve national, EU and non-EU stakeholders compose complex Supply Chain Services (SCS). The security of the SCS is most important and it emphasized in the NIS 2 directive [3] and it is a shared responsibility of all stakeholders involved that will need to be compliant with a scheme. In this paper we present an overview of the proposed Cybersecurity Certification Scheme for Supply Chain Services (EUSCS) as proposed by the European Commission (EC) project CYRENE [1]. The EUSCS scheme covers all the three assurance levels defined in the Cybersecurity Act (CSA) [2] taking into consideration the criticality of SCS according to the NIS 2 directive [3], the ENISA Threat Landscape for Supply Chain Attacks [4] and the CYRENE extended online Information Security Management System (ISMS) that allows all SCS stakeholders to provide and access all information needed for certification purposes making the transition from current national schemes in the EU easier.

The paper deals with exponentially growing technology - Internet of Things (IoT) in the field of healthcare. It is spoken about the networked healthcare and medical architecture. The attention is given to the analysis of the international regulations on medical and healthcare cybersecurity. For building a trustworthy healthcare IoT solution, a developed normative hierarchical model of the international cybersecurity standards is provided. For cybersecurity assessment of such systems the case-oriented technique, which includes Advanced Security Assurance Case (ASAC) and an example on a wireless insulin pump of its application are provided.

Embedded Systems (ES) development has been historically focused on functionality rather than security, and today it still applies in many sectors and applications. However, there is an increasing number of security threats over ES, and a successful attack could have economical, physical or even human consequences, since many of them are used to control critical applications. A standardized and general accepted security testing framework is needed to provide guidance, common reporting forms and the possibility to compare the results along the time. This can be achieved by introducing security metrics into the evaluation or assessment process. If carefully designed and chosen, metrics could provide a quantitative, repeatable and reproducible value that would reflect the level of security protection of the ES. This paper analyzes the features that a good security metric should exhibit, introduces a taxonomy for classifying them, and finally, it carries out a literature survey on security metrics for the security evaluation of ES. In this review, more than 500 metrics were collected and analyzed. Then, they were reduced to 169 metrics that have the potential to be applied to ES security evaluation. As expected, the 77.5% of them is related exclusively to software, and only the 0.6% of them addresses exclusively hardware security. This work aims to lay the foundations for constructing a security evaluation methodology that uses metrics so as to quantify the security level of an ES.

Complex military systems are typically cyber-physical systems which are the targets of high level threat actors, and must be able to operate within a highly contested cyber environment. There is an emerging need to provide a strong level of assurance against these threat actors, but the process by which this assurance can be tested and evaluated is not so clear. This paper outlines an initial framework developed through research for evaluating the cyber-worthiness of complex mission critical systems using threat models developed in SysML. The framework provides a visual model of the process by which a threat actor could attack the system. It builds on existing concepts from system safety engineering and expands on how to present the risks and mitigations in an understandable manner.

From financial services platforms to social networks to vehicle control, software has come to mediate many activities of daily life. Governing bodies and standards organizations have responded to this trend by creating regulations and standards to address issues such as safety, security and privacy. In this environment, the compliance of software development to standards and regulations has emerged as a key requirement. Compliance claims and arguments are often captured in assurance cases, with linked evidence of compliance. Evidence can come from testcases, verification proofs, human judgement, or a combination of these. That is, we try to build (safety-critical) systems carefully according to well justified methods and articulate these justifications in an assurance case that is ultimately judged by a human. Yet software is deeply rooted in uncertainty making pragmatic assurance more inductive than deductive: most of complex open-world functionality is either not completely specifiable (due to uncertainty) or it is not cost-effective to do so, and deductive verification cannot happen without specification. Inductive assurance, achieved by sampling or testing, is easier but generalization from finite set of examples cannot be formally justified. And of course the recent popularity of constructing software via machine learning only worsens the problem - rather than being specified by predefined requirements, machine-learned components learn existing patterns from the available training data, and make predictions for unseen data when deployed. On the surface, this ability is extremely useful for hard-to specify concepts, e.g., the definition of a pedestrian in a pedestrian detection component of a vehicle. On the other, safety assessment and assurance of such components becomes very challenging. In this talk, I focus on two specific approaches to arguing about safety and security of software under uncertainty. The first one is a framework for managing uncertainty in assurance cases (for "conventional" and "machine-learned" systems) by systematically identifying, assessing and addressing it. The second is recent work on supporting development of requirements for machine-learned components in safety-critical domains.

Cybersecurity assurance plays an important role in managing trust in smart grid communication systems. In this paper, cybersecurity assurance controls for smart grid communication networks and devices are delineated from the more technical functional controls to provide insights on recent innovative risk-based approaches to cybersecurity assurance in smart grid systems. The cybersecurity assurance control baselining presented in this paper is based on requirements and guidelines of the new family of IEC 62443 standards on network and systems security of industrial automation and control systems. The paper illustrates how key cybersecurity control baselining and tailoring concepts of the U.S. NIST SP 800-53 can be adopted in smart grid security architecture. The paper outlines the application of IEC 62443 standards-based security zoning and assignment of security levels to the zones in smart grid system architectures. To manage trust in the smart grid system architecture, cybersecurity assurance base lining concepts are applied per security impact levels. Selection and justification of security assurance controls presented in the paper is utilizing the approach common in Security Technical Implementation Guides (STIGs) of the U.S. Defense Information Systems Agency. As shown in the paper, enhanced granularity for managing trust both on the overall system and subsystem levels of smart grid systems can be achieved by implementation of the instructions of the CNSSI 1253 of the U.S. Committee of National Security Systems on security categorization and control selection for national security systems.

With the increasing use of mobile phones in contemporary society, more and more networked computers are connected to each other. This has brought along security issues. To solve these issues, both research and development communities are trying to build more secure software. However, there is the question that how the secure software is defined and how the security could be measured. In this paper, we study this problem by studying what kinds of security measurement tools (i.e. metrics) are available, and what these tools and metrics reveal about the security of software. As the result of the study, we noticed that security verification activities fall into two main categories, evaluation and assurance. There exist 34 metrics for measuring the security, from which 29 are assurance metrics and 5 are evaluation metrics. Evaluating and studying these metrics, lead us to the conclusion that the general quality of the security metrics are not in a satisfying level that could be suitably used in daily engineering work flows. They have both theoretical and practical issues that require further research, and need to be improved.