Selecting Countermeasures for ICT systems Before They are Attacked

A countermeasure is any change to a system to reduce the probability it is successfully attacked. We propose a model based approach that selects countermeasures through multiple simulations of the behaviors of an ICT system and of intelligent attackers that implement sequences of attacks. The simulations return information on the attacker sequences and the goals they reach we use to compute the statistics that drive the selection. Since attackers change their sequences as countermeasures are deployed, we have defined an iterative strategy where each iteration selects some countermeasures, updates the system models and runs the simulations to discover any new attacker sequence. The discovery of new sequences starts a new iteration. The Haruspex suite automates the proposed approach. Some of its tools acquire information on the target system and on the attackers and build the corresponding models. Another tool simulates the attacks through the models of the system and of the attackers. The tool to select countermeasures invokes the other ones to discover how countermeasures influence the attackers. We apply the whole suite to three systems and discuss how the connection topology influences the countermeasures to adop

SLA-Based Continuous Security Assurance in Multi-Cloud DevOps

Multi-cloud applications, i.e. those that are deployed over multiple independent Cloud providers, pose a number of challenges to the security-aware development and operation. Security assurance in such applications is hard due to the lack of insights of security controls ap- plied by Cloud providers and the need of controlling the security levels of all the components and layers at a time. This paper presents the MUSA approach to Service Level Agreement (SLA)-based continuous security assurance in multi-cloud applications. The paper details the proposed model for capturing the security controls in the o ered application Se- curity SLA and the approach to continuously monitor and asses the controls at operation phase. This new approach enables to easily align development security requirements with controls monitored at operation as well as early react at operation to any possible security incident or SLA violation.The MUSA project leading to this paper has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 644429

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)

How Physicality Enables Trust: A New Era of Trust-Centered Cyberphysical Systems

Multi-agent cyberphysical systems enable new capabilities in efficiency, resilience, and security. The unique characteristics of these systems prompt a reevaluation of their security concepts, including their vulnerabilities, and mechanisms to mitigate these vulnerabilities. This survey paper examines how advancement in wireless networking, coupled with the sensing and computing in cyberphysical systems, can foster novel security capabilities. This study delves into three main themes related to securing multi-agent cyberphysical systems. First, we discuss the threats that are particularly relevant to multi-agent cyberphysical systems given the potential lack of trust between agents. Second, we present prospects for sensing, contextual awareness, and authentication, enabling the inference and measurement of ``inter-agent trust" for these systems. Third, we elaborate on the application of quantifiable trust notions to enable ``resilient coordination," where ``resilient" signifies sustained functionality amid attacks on multiagent cyberphysical systems. We refer to the capability of cyberphysical systems to self-organize, and coordinate to achieve a task as autonomy. This survey unveils the cyberphysical character of future interconnected systems as a pivotal catalyst for realizing robust, trust-centered autonomy in tomorrow's world