As the deployment of Internet of Things (IoT) is experiencing an exponential growth, it is no surprise that many recent cyber attacks are IoT-enabled: The attacker initially exploits some vulnerable IoT technology as a first step towards compromising a critical system that is connected, in some way, with the IoT. For some sectors, like industry, smart grids, transportation and medical services, the significance of such attacks is obvious, since IoT technologies are part of critical back-end systems. However, in sectors where IoT is usually at the end-user side, like smart homes, such attacks can be underestimated, since not all possible attack paths are examined. In this paper we survey IoT-enabled cyber attacks, found in all application domains since 2010. For each sector, we emphasize on the latest, verified IoT-enabled attacks, based on known real-world incidents and published proof-of-concept attacks. We methodologically analyze representative attacks that demonstrate direct, indirect and subliminal attack paths against critical targets. Our goal is threefold: (i) To assess IoT-enabled cyber attacks in a risk-like approach, in order to demonstrate their current threat landscape; (ii) To identify hidden and subliminal IoT-enabled attack paths against critical infrastructures and services, and (iii) To examine mitigation strategies for all application domains.

The Internet of Medical Things (IoMT) couples IoT technologies with healthcare services in order to support real-time, remote patient monitoring and treatment. However, the interconnectivity of critical medical devices with other systems in various network layers creates new opportunities for remote adversaries. Since most of the communication protocols have not been specifically designed for the needs of connected medical devices, there is a need to classify the available IoT communication technologies in terms of security. In this paper we classify IoT communication protocols, with respect to their application in IoMT. Then we describe the main characteristics of IoT communication protocols used at the perception, network and application layer of medical devices. We examine the inherent security characteristics and limitations of IoMT-specific communication protocols. Based on realistic attacks we identify available mitigation controls that may be applied to secure IoMT communications, as well as existing research and implementation gaps.

Dependency analysis of critical infrastructures is a computationally intensive problem when dealing with large-scale, cross-sectoral, cascading and common-cause failures. The problem intensifies when attempting a dynamic, time-based dependency analysis. This paper extends a previous graph-based risk analysis methodology to dynamically assess the evolution of cascading failures over time. Various growth models are employed to capture slow, linear and rapidly evolving effects, but instead of using static projections, the evolution of each dependency is “objectified” by a fuzzy system that also considers the effects of nearby dependencies. To achieve this, the impact (and, eventually, risk) of each dependency is quantified on the time axis into a form of many-valued logic. In addition, the methodology is extended to analyze major failures triggered by concurrent common-cause cascading events. A critical infrastructure dependency analysis tool, CIDA, that implements the extended risk-based methodology is described. CIDA is designed to assist decision makers in proactively analyzing dynamic and complex dependency risk paths in two ways: (i) identifying potentially underestimated low risk dependencies and reclassifying them to a higher risk category before they are realized; and (ii) simulating the effectiveness of alternative mitigation controls with different reaction times. Thus, the CIDA tool can be used to evaluate alternative defense strategies for complex, large-scale and multi-sectoral dependency scenarios and to assess their resilience in a cost-effective manner.JRC.G.5-Security technology assessmen

Dependency risk graphs have been proposed as a tool for analyzing cascading failures due to critical infrastructure dependency chains. However, dependency chain analysis is not by itself adequate to develop an efficient risk mitigation strategy – one that specifies which critical infrastructures should have high priority for applying mitigation controls in order to achieve an optimal reduction in the overall risk. This paper extends previous dependency risk analysis research to implement efficient risk mitigation. This is accomplished by exploring the relation between dependency risk paths and graph centrality characteristics. Graph centrality metrics are applied to design and evaluate the effectiveness of alternative risk mitigation strategies. The experimental evaluations are based on random graphs that simulate common critical infrastructure dependency characteristics as identified by recent empirical studies. The experimental results are used to specify an algorithm that prioritizes critical infrastructure nodes for applying controls in order to achieve efficient risk mitigation.JRC.G.5-Security technology assessmen