Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems, cyber risk at the edge

The Internet of Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state of the art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analysis is presented as the current and a target risk state for IoT systems, followed by conclusions and recommendations on a transformation roadmap, describing how IoT systems can achieve the target state with a new goal-oriented dependency model. By target state, we refer to the cyber security target that matches the generic security requirements of an organisation. The research paper studies and adapts four alternatives for IoT risk assessment and identifies the goal-oriented dependency modelling as a dominant approach among the risk assessment models studied. The new goal-oriented dependency model in this article enables the assessment of uncontrollable risk states in complex IoT systems and can be used for a quantitative self-assessment of IoT cyber risk posture

Security risk assessment in Internet of Things systems

Information security risk assessment methods have served us well over the past two decades. They have provided a tool for organizations and governments to use in protecting themselves against pertinent risks. As the complexity, pervasiveness, and automation of technology systems increases and cyberspace matures, particularly with the Internet of Things (IoT), there is a strong argument that we will need new approaches to assess risk and build trust. The challenge with simply extending existing assessment methodologies to IoT systems is that we could be blind to new risks arising in such ecosystems. These risks could be related to the high degrees of connectivity present or the coupling of digital, cyber-physical, and social systems. This article makes the case for new methodologies to assess risk in this context that consider the dynamics and uniqueness of the IoT while maintaining the rigor of best practice in risk assessment

Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems: cyber risk at the edge

AbstractThe Internet of Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state of the art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analysis is presented as the current and a target risk state for IoT systems, followed by conclusions and recommendations on a transformation roadmap, describing how IoT systems can achieve the target state with a new goal-oriented dependency model. By target state, we refer to the cyber security target that matches the generic security requirements of an organisation. The research paper studies and adapts four alternatives for IoT risk assessment and identifies the goal-oriented dependency modelling as a dominant approach among the risk assessment models studied. The new goal-oriented dependency model in this article enables the assessment of uncontrollable risk states in complex IoT systems and can be used for a quantitative self-assessment of IoT cyber risk posture.</jats:p

Continuous Quantitative Risk Management in Smart Grids Using Attack Defense Trees

Although the risk assessment discipline has been studied from long ago as a means to support security investment decision-making, no holistic approach exists to continuously and quantitatively analyze cyber risks in scenarios where attacks and defenses may target different parts of Internet of Things (IoT)-based smart grid systems. In this paper, we propose a comprehensive methodology that enables informed decisions on security protection for smart grid systems by the continuous assessment of cyber risks. The solution is based on the use of attack defense trees modelled on the system and computation of the proposed risk attributes that enables an assessment of the system risks by propagating the risk attributes in the tree nodes. The method allows system risk sensitivity analyses to be performed with respect to different attack and defense scenarios, and optimizes security strategies with respect to risk minimization. The methodology proposes the use of standard security and privacy defense taxonomies from internationally recognized security control families, such as the NIST SP 800-53, which facilitates security certifications. Finally, the paper describes the validation of the methodology carried out in a real smart building energy efficiency application that combines multiple components deployed in cloud and IoT resources. The scenario demonstrates the feasibility of the method to not only perform initial quantitative estimations of system risks but also to continuously keep the risk assessment up to date according to the system conditions during operation.This research leading to these results was funded by the EUROPEAN COMMISSION, grant number 787011 (SPEAR Horizon 2020 project) and 780351 (ENACT Horizon 2020 project)

Continuous Quantitative Risk Management in Smart Grids Using Attack Defense Trees

Although the risk assessment discipline has been studied from long ago as a means to support security investment decision-making, no holistic approach exists to continuously and quantitatively analyze cyber risks in scenarios where attacks and defenses may target different parts of Internet of Things (IoT)-based smart grid systems. In this paper, we propose a comprehensive methodology that enables informed decisions on security protection for smart grid systems by the continuous assessment of cyber risks. The solution is based on the use of attack defense trees modelled on the system and computation of the proposed risk attributes that enables an assessment of the system risks by propagating the risk attributes in the tree nodes. The method allows system risk sensitivity analyses to be performed with respect to different attack and defense scenarios, and optimizes security strategies with respect to risk minimization. The methodology proposes the use of standard security and privacy defense taxonomies from internationally recognized security control families, such as the NIST SP 800-53, which facilitates security certifications. Finally, the paper describes the validation of the methodology carried out in a real smart building energy efficiency application that combines multiple components deployed in cloud and IoT resources. The scenario demonstrates the feasibility of the method to not only perform initial quantitative estimations of system risks but also to continuously keep the risk assessment up to date according to the system conditions during operation.This research leading to these results was funded by the EUROPEAN COMMISSION, grant number 787011 (SPEAR Horizon 2020 project) and 780351 (ENACT Horizon 2020 project)

Continuous Quantitative Risk Management in Smart Grids Using Attack Defense Trees

Although the risk assessment discipline has been studied from long ago as a means to support security investment decision-making, no holistic approach exists to continuously and quantitatively analyze cyber risks in scenarios where attacks and defenses may target different parts of Internet of Things (IoT)-based smart grid systems. In this paper, we propose a comprehensive methodology that enables informed decisions on security protection for smart grid systems by the continuous assessment of cyber risks. The solution is based on the use of attack defense trees modelled on the system and computation of the proposed risk attributes that enables an assessment of the system risks by propagating the risk attributes in the tree nodes. The method allows system risk sensitivity analyses to be performed with respect to different attack and defense scenarios, and optimizes security strategies with respect to risk minimization. The methodology proposes the use of standard security and privacy defense taxonomies from internationally recognized security control families, such as the NIST SP 800-53, which facilitates security certifications. Finally, the paper describes the validation of the methodology carried out in a real smart building energy efficiency application that combines multiple components deployed in cloud and IoT resources. The scenario demonstrates the feasibility of the method to not only perform initial quantitative estimations of system risks but also to continuously keep the risk assessment up to date according to the system conditions during operation.This research leading to these results was funded by the EUROPEAN COMMISSION, grant number 787011 (SPEAR Horizon 2020 project) and 780351 (ENACT Horizon 2020 project)

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

Dynamic risk assessment in IT environments: a decision guide

Security and reliability of information technologies have emerged as major concerns nowadays. Risk assessment, an estimation of negative impacts that might be imposed to a network by a series of potential sources, is one of the main tasks to ensure the security and is performed either statically or dynamically. Static risk assessment cannot satisfy the requirements of real-time and ubiquitous computing networks as it is pre-planned and does not consider upcoming changes such as the creation of new attack strategies. However, dynamic risk assessment (DRA) considers real-time evidences, being capable of diagnosing abnormal events in changing environments. Several DRA approaches have been proposed recently, but it is unclear which technique fits best into IT scenarios with different requirements. Thus, this chapter introduces recent trends in DRA, by analyzing 27 works and proposes a decision guide to help IT managers in choosing the most suitable DRA technique considering three illustrative scenarios – regular computer networks, internet of things, and industrial control systems

Toward the automation of threat modeling and risk assessment in IoT systems

The Internet of Things (IoT) has recently become one of the most relevant emerging technologies in the IT landscape. IoT systems are characterized by the high heterogeneity of involved architectural components (e.g., device platforms, services, networks, architectures) and involve a multiplicity of application domains. In the IoT scenario, the identification of specific security requirements and the security design are very complex and expensive tasks, since they heavily depend on the configuration deployment actually in place and require security experts. In order to overcome these issues, we propose an approach aimed at supporting the security analysis of an IoT system by means of an almost completely automated process for threat modeling and risk assessment, which also helps identify the security controls to implement in order to mitigate existing security risks. We demonstrate the effectiveness of the approach by discussing its application to a home automation system, built on top of commercial IoT products