From Monitoring Templates to Security Monitoring and Threat Detection

This paper presents our pattern-based approach to run-time requirements monitoring and threat detection being developed as part of an approach to build frameworks supporting the construction of secure and dependable systems for ambient intelligence. Our patterns infra-structure is based on templates. From templates we generate event-calculus formulas expressing security requirements to monitor at run-time. From these theories we generate attack signatures, describing threats or possible attacks to the system. At run-time, we evaluate the likelihood of threats from run-time observations using a probabilistic model based on Bayesian networks

Fault Tolerance Using an SDN Pattern Framework

Software Defined Networking (SDN) and Network Function Virtualization (NFV) are a promising combination for programmable connectivity, rapid service provisioning and service chaining as they offer the necessary end-to-end optimizations. However, with the actual exponential growth of connected devices, future networks such as SDN/NFV require an open-solutions architecture, facilitated by standards and a strong ecosystem. Such networks need to support communication services that offers guarantees about fault tolerance, redundancy, resilience and security. The construction of complex networks preserving Security and Dependability (S&D) properties is necessary to avoid system vulnerabilities, which may occur in the various layers of SDN architectures. In this work, we propose a pattern framework build in an SDN controller able to import design patterns in a rule-based language in order to provide fault tolerance in SDN networks. To evaluate the importance and the functionality of this framework, fault tolerance patterns are proposed to guarantee network connectivity, detection and restoration of network traffic in SDN network infrastructures

Towards a Security, Privacy, Dependability, Interoperability Framework for the Internet of Things

A popular application of ambient intelligence systems constitutes of assisting living services on smart buildings. As intelligence is imported in embedded equipment, the system becomes able to provide smart services (e.g. control lights, airconditioning, provide energy management services etc.). IoT is the main enabler of such environments. However, the interconnection of these cyber-physical systems and the processing of personal data raise serious security and privacy issues. In this paper we present a framework that can guarantee Security, Privacy, Dependability and Interoperability (SPDI) in IoT. Taking advantage of the underlying IoT deployment, the proposed framework not only implements the requested smart functionality but also provide modelling and administration that can guarantee those SPDI properties. Moreover, we provide an application example of the framework in a smart building scenario

Threat Landscape and Good Practice Guide for Software Defined Networks/5G

5G represents the next major phase of mobile telecommunication systems and network architectures beyond the current 4G standards, aiming at extreme broadband and ultra-robust, low latency connectivity, to enable the programmable connectivity for the Internet of Everything2. Despite the significant debate on the technical specifications and the technological maturity of 5G, which are under discussion in various fora3, 5G is expected to affect positively and significantly several industry sectors ranging from ICT to industry sectors such as car and other manufacturing, health and agriculture in the period up to and beyond 2020. 5G will be driven by the influence of software on network functions, known as Software Defined Networking (SDN) and Network Function Virtualization (NFV). The key concept that underpins SDN is the logical centralization of network control functions by decoupling the control and packet forwarding functionality of the network. NFV complements this vision through the virtualization of these functionalities based on recent advances in general server and enterprise IT virtualization. Considering the technological maturity of the technologies that 5G can leverage on, SDN is the one that is moving faster from development to production. To realize the business potential of SDN/5G, a number of technical issues related to the design and operation of Software Defined Networks need to be addressed. Amongst them, SDN/5G security is one of the key issues, that needs to be addressed comprehensively in order to avoid missing the business opportunities arising from SDN/5G. In this report, we review threats and potential compromises related to the security of SDN/5G networks. More specifically, this report contains a review of the emerging threat landscape of 5G networks with particular focus on Software Defined Networking. It also considers security of NFV and radio network access. To provide a comprehensive account of the emerging threat SDN/5G landscape, this report has identified related network assets and the security threats, challenges and risks arising for these assets. Driven by the identified threats and risks, this report has also reviewed and identified existing security mechanisms and good practices for SDN/5G/NFV, and based on these it has analysed gaps and provided technical, policy and organizational recommendations for proactively enhancing the security of SDN/5G

Public health policy for management of hearing impairments based on big data analytics: EVOTION at Genesis

The holistic management of hearing loss (HL) requires appropriate public health policies for HL prevention, early diagnosis, long-term treatment and rehabilitation; detection and prevention of cognitive decline; protection from noise; and socioeconomic inclusion of HL patients. However, currently the evidential basis for forming such policies is limited. Holistic HL management policies require the analysis of heterogeneous data, including Hearing Aid (HA) usage, noise episodes, audiological, physiological, cognitive, clinical and medication, personal, behavioural, life style, occupational and environmental data. To utilise these data in forming holistic HL management policies, EVOTION, a new European research and innovation project, aims to develop an integrated platform supporting: (a) the analysis of related datasets to enable the identification of causal and other effects amongst them using various forms of big data analytics, (b) policy decision making focusing on the selection of effective interventions related to the holistic management of HL, based on the outcomes of (a) and the formulation of related public health policies, and (c) the specification and monitoring of such policies in a sustainable manner. In this paper, we describe the EVOTION approach

Associations Between Hearing Performance and Physiological Measures - An Overview and Outlook

The current paper summarises the research investigating associations between physiological data and hearing performance. An overview of state-of-the-art research and literature is given as well as promising directions for associations between physiological data and data regarding hearing loss and hearing performance. The physiological parameters included in this paper are: electrodermal activity, heart rate variability, blood pressure, blood oxygenation and respiratory rate. Furthermore, the environmental and behavioural measurements of physical activity and body mass index, alcohol consumption and smoking have been included. So far, only electrodermal activity and heart rate variability are physiological signals simultaneously associated with hearing loss or hearing performance. Initial findings suggest blood pressure and respiratory rate to be the most promising physiological measures that relate to hearing loss and hearing performance

Plasticity induced by pairing brain stimulation with motor-related states only targets a subset of cortical neurones

Movement-related brain stimulation (MRBS) interventions associate endogenously generated movement-related brain states with external brain stimuli to induce targeted plastic changes in the motor cortex (M1) [[1], [2], [3], [4]]. These studies have emphasised the importance of the timing of stimulation relative to movement onset. However, none has examined whether the effects are specific to the cortical circuits activated by the stimuli. The question arises because previous work has shown that different sets of inputs to corticospinal neurones can be activated using TMS. Stimulation with a posterior-anterior (PA) direction activates a set of neurones that have a shorter latency connection to corticospinal neurones than those activated with an anterior-posterior (AP) current [5]. Previous MRBS studies have paired movement onset with PA pulses [1]. The present work tests whether the after-effects of MRBS are specific to PA-sensitive neurones, or whether those activated by AP pulses are also affected. Here we applied AP or PA TMS pulses applied just prior to the onset of volitional index finger movements in two experiments conducted on separate days in the same group of individuals [3]. Corticospinal excitability changes induced by these interventions were assessed using AP and PA TMS pulses in the effector muscle and in a control muscle

Pairing a Circular Economy and the 5G-Enabled Internet of Things: Creating a Class of “Looping Smart Assets”

The increase in the world’s population has led to a massive rise in human consumption of the planet’s natural resources, well beyond their replacement rate. Traditional recycling concepts and methods are not enough to counter such effects. In this context, a circular economy (CE), that is, a restorative and regenerative by-design economy, can reform today’s “take–make–dispose” economic model. On the other hand, the Internet of Things (IoT) continues to gradually transform our everyday lives, allowing for the introduction of novel types of services while enhancing legacy ones. Taking this as our motivation, in this article we analyze the CE/IoT interplay, indicating innovative ways in which this interaction can drastically affect products and services, their underlying business models, and the associated ecosystems. Moreover, we present an IoT architecture that enables smart object integration into the IoT ecosystem. The presented architecture integrates circularityenabling features by maximizing the exploitation of assets toward a new type of IoT ecosystem that is circular by design (CbD). Finally, we provide a proof-of-concept implementation and an application study of the proposed architecture and results regarding the applicability of the proposed approach for the telecommunications (telecom) sector

The green blockchains of circular economy

Eco‐friendly systems are necessitated nowadays, as the global consumption is increasing. A data‐driven aspect is prominent, involving the Internet of Things (IoT) as the main enabler of a Circular Economy (CE). Henceforth, IoT equipment records the system’s functionality, with machine learning (ML) optimizing green computing operations. Entities exchange and reuse CE assets. Transparency is vital as the beneficiaries must track the assets’ history. This article proposes a framework where blockchaining administrates the cooperative vision of CE‐IoT. For the core operation, the blockchain ledger records the changes in the assets’ states via smart contracts that implement the CE business logic and are lightweight, complying with the IoT requirements. Moreover, a federated learning approach is proposed, where computationally intensive ML tasks are distributed via a second contract type. Thus, “green‐miners” devote their resources not only for making money, but also for optimizing operations of real‐systems, which results in actual resource savings

Fully Synthetic Longitudinal Real-World Data From Hearing Aid Wearers for Public Health Policy Modeling

Here, we share the first outcome of EVOTION (www.h2020evotion.eu) in the form of a data-set to inspire, encourage, and motivate a data-driven analytical approach to evidence-based healthcare policy modeling using real-world longitudinal data. The data-set includes information relating to patterns of real-world hearing aid usage and sound environment exposure. Undoubtedly, many such data-sources will be available for researchers and policy-makers in the future, and the data-set presented here can act as a first step of building and testing potential statistical model