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In recent years, distributed ledger technologies (DLTs) and blockchain have become disruptive technologies to support distributed and trusted sharing ecosystems in various domains. Among the potential scenarios that can leverage their benefits, cooperative intelligent transport systems (C-ITS) and autonomous vehicles (AV) represent a key trend of the next digital era to build a safer society. However, different aspects such as performance and practical issues, as well as conformance with current standards and legislation, may hinder the adoption of DLT in such scenarios. This article analyses the potential applications that could leverage DLTs features and the challenges to be overcome in the coming years to foster the adoption of DLTs in C-ITS and AV. Through this analysis, we additionally provide a set of potential research directions and ways forward to exploit the advantages of DLTs in C-ITS and AV in terms of decentralized trust and transparency.

With the continuous development of distributed ledger and blockchain technologies, new use cases apart from cryptocurrencies have come into the spotlight. In this article, we evaluate whether an e-government service could be a suitable candidate for a blockchain transformation. We selected as a reference test system an existing cross-border e-government service that is used for supporting goods exchanges across the European Union. We show how such an indicative paradigm can be transformed into a blockchain system. In order to do so, we deployed it in an emulated architecture for evaluating its performance under various realistic conditions. Our results show that the deployed system is able to meet the requirements, both in terms of throughput and transaction speed. Moreover, it shows clear advantages in terms of usability and synchronization between all entities.

A data-driven society requires a common regulatory umbrella to provide a harmonized vision of cybersecurity. We describe cybersecurity policies and joint initiatives in the European Union and give insights about the need to align ongoing technological advances with such regulatory efforts.

The authors investigate the application of deep convolutional neural networks (CNNs) to the problem of radiometric identification, i.e. the task of authenticating wireless devices on the basis of their radio frequency (RF) emissions, which contain features directly related to the physical properties of the wireless devices. They collected digitised RF from 12 wireless devices, and used various techniques to transform the time series derived from the RF to images. A deep CNN is then applied to the images. The authors' results show that the identification performance of the combination of deep CNN with an image representation significantly outperforms conventional methods based on dissimilarity on the original time series. Moreover, a specific comparison among RF-to-image techniques show that on their datasets the wavelet-based approach outperforms other approaches, also in the presence of white Gaussian noise.

Extensive research has been performed in recent years for the identification of wireless devices from their radio frequency (RF) emissions. The main idea of identifying a wireless device through its RF emissions is that the electronic circuits and the RF components have specific characteristics determined by the production and manufacturing processes. These characteristics, which result in unique differences, can be used to distinguish a wireless device from another because they appear as subtle modification of the RF signal in space even if the wireless device generates a signal conformant to the standard. This chapter describes the main techniques for the fingerprinting of wireless devices using their RF transmission. There are still some key challenges to overcome. This chapter tries to identify them in this context as well as providing possible approaches to solve them. Further research work is needed to investigate the portability issues between fingerprints taken using different receivers, as well as to identify and remove potential other sources of bias.

This paper addresses the problem of authentication and identification of wireless devices using their physical properties derived from their Radio Frequency (RF) emissions. This technique is based on the concept that small differences in the physical implementation of wireless devices are significant enough and they are carried over to the RF emissions to distinguish wireless devices with high accuracy. The technique can be used both to authenticate the claimed identity of a wireless device or to identify one wireless device among others. In the literature, this technique has been implemented by feature extraction in the 1D time domain, 1D frequency domain or also in the 2D time frequency domain. This paper describes the novel application of the synchrosqueezing transform to the problem of physical layer authentication. The idea is to exploit the capability of the synchrosqueezing transform to enhance the identification and authentication accuracy of RF devices from their actual wireless emissions. An experimental dataset of 12 cellular communication devices is used to validate the approach and to perform a comparison of the different techniques. The results described in this paper show that the accuracy obtained using 2D Synchrosqueezing Transform (SST) is superior to conventional techniques from the literature based in the 1D time domain, 1D frequency domain or 2D time frequency domain.

The concept of Radio Frequency (RF) fingerprinting is that electronic devices can be identified and authenticated through their radio frequency emissions, which contain intrinsic features of the device itself. RF fingerprinting can be used to enhance the security of wireless networks because the fingerprints provide a form of authentication. In previous research papers, the RF fingerprints have typically been obtained by extracting statistical features from the time series generated by the analog-to-digital conversion of the RF emissions. In this paper, we investigate a novel approach to the RF fingerprinting of Internet of Things (IOT) devices, where the time series are converted into images, out of which image processing features are extracted. The performance of this approach is experimentally evaluated by applying different machine learning algorithms on different types of conversions of time series to images. Our analysis shows that the proposed approach provides a better identification accuracy as compared to the accuracy achieved by conventional sets of statistical features used in the literature. Even if relatively small (around 1%), this accuracy improvement is statistically significant when classification is repeated over different folds of the training and test data. Yet, this enhanced accuracy is obtained at the cost of the longer time taken to process the images.

Micro-generation promises to greatly contribute to the energy balance of the energy grid; however, so far, its market penetration is going slow due to the few, or not-existing, direct economic benefits end-users would enjoy by deploying an in-house micro-generation system. In this paper, taking advantage of the potentialities of blockchain technologies, we propose a solar energy production and distribution architecture using smart contracts, a particular distributed ledger paradigm, to support automatic energy exchanges and auctions, potentially enabling a new, open and more fruitful, under an end-user perspective, energy micro-generation market. We present the conceptual design of the approach, as well the energy grid prototype and the control layer, running on the Ethereum platform. The proposed architecture has been implemented and validated through an in-house developed test-bed.

Internet of Things (IoT) can support numerous applications and services in various domains, such as smart cities and smart homes. IoT smart objects interact with other components e.g., proxies, mobile devices, and data collectors, for management, data sharing and other activities in the context of the provided service. Though such components contribute to address various societal challenges and provide new advanced services for users, their limited processing capabilities make them vulnerable to well-known security and privacy threats. Until now various research works have studied security and privacy in IoT, validating this claim. However, to the best of our knowledge literature lacks research focusing on security and privacy flaws introduced in IoT through interactions among different devices supporting a smart home architecture. In particular, we set up the scene for a security and privacy threat analysis for a typical smart home architecture using off the shelf components. To do so, we employ a smart home IoT architecture that enables users to interact with it through various devices that support smart house management, and we analyze different scenarios to identify possible security and privacy issues for users.

The identification of mobile phones through their built-in electronic sensors has been proven in literature for cameras, microphones and accelerometers demonstrating to have various applications in security, forensics or in the fight against the counterfeiting of electronic products. The identification of a sensor (and consequently of the mobile phone) is possible through the exploitation of small but significant differences in the physical components of the sensor itself. These physical differences are mainly generated during the production process of the sensor in the manufacturing plant producing small but reproducible variations in the digital output generated by the built-in sensor. In particular, in this paper, we investigate the identification of mobile phones through the built-in magnetometer sensor, which has received very limited attention by the research community so far. In particular, the specific aspects of robustness and portability of such a fingerprint have been analyzed. Different stimulation conditions, diverse kinds of features and classification procedures have been considered achieving very promising results.

The Informed Consent of a data subject (e.g., citizen) is often necessary to allow the legitimate processing of personal data by a third party application. The current implementation of Informed Consent based on End User License Agreements (EULA) has many limitations, which are likely to become more critical in future IoT applications, where the collection of personal data can happen in various ways and is not evident to the user. There is the need to define more sophisticated models of Informed Consent for IoT, which address the specific features of IoT, improve on the EULA approach, and protect the flow of personal data from the IoT sensors. In this paper, we propose an agent-based design for Informed Consent in IoT, where access to personal data is regulated through usage control policies, which can be tailored for the specific features of the user and the context. Policies are associated to users, service providers, and smart spaces containing IoT devices in a privacy-friendly way using pseudonyms. The main design concepts are described and applied to a smart city scenario, to evaluate the feasibility of the framework and the related deployment aspects.

The concept of Smart Home where appliances, sensors, actuators, displays and computing resources are connected and interact to support the life of the citizen is being increasingly researched. In this context, the Wi-Fi communication technology has grown to become the de-facto standard for data communications in Smart Home environments, with cordless telephony being dominated by the DECT protocol. Even though both technologies incorporate sets of security features aimed at securing the confidentiality and integrity of the communications, the nature and the design of both radio-frequency protocols make them vulnerable, up to a certain extent, to privacy leakages through traffic analysis attacks. In this paper we explore the information leakage vulnerabilities inherent to these technologies and their potential impact on citizens’ privacy in the context of the Smart Home. We demonstrate how the websites visited by a smart device can be inferred by applying machine learning and pattern matching techniques to eavesdropped encrypted traffic. Keywords—Privacy, Wireless communications, Machine learning

According to the European Union data protection legislation, privacy is a fundamental right that should be protected in the interaction of the citizen with the digital world. In the evolution of Internet towards new paradigms like Internet of Things (IoT), protection of privacy can be a challenging task because IoT connected objects can generate an enormous amount of data, some of which actually constitute personal data. In addition, it is difficult to control the flow of data when there is no user interface or adequate tools for the user. In this paper we describe an efficient solution to enforcement security policy rules that addresses this challenge, and takes a more general enterprise architecture approach for security and privacy engineering in IoT. This enforcement solution is based on a Model-based Security Toolkit named SecKit, and its integration with the MQ Telemetry Transport (MQTT) protocol layer, which is a widely adopted technology to enable the communication between IoT devices. In this paper, we describe the motivation and design of our enforcement solution, demonstrating its feasibility and the performance results in a case study., JRC.G.6-Digital Citizen Security

The ongoing evolution of the Internet towards the Internet of Things (IoT) is creating new exciting markets and applications, but also poses new questions on the relationship between the human subjects and the IoT devices and services. Connected medical devices, wearable sensors, smart cars and smart appliances will become more and more extensions of the human body and mind, enhancing the synergies between humans, the physical, and the digital world. In this context, there is the risk that humans increasingly shift or delegate their freedom and capability to act (agency) to IoT “things”, which have unclear or undefined levels of trust. There is the need to create “more robustly” trusted and effective digital relationships, where the users can maintain their control on the IoT services and the generated flows of data. In addition, they should have reliable knowledge of the levels of trust provided by the IoT technologies. In this paper, we discuss how these new relationships should be formed, we identify the main challenges for building trust in IoT and we propose a model-based approach for risk, trust, and usage control policies. In our vision, the integration of specific technological tools, coupled with a high level of choice for the citizen/user can be used to mitigate these challenges., JRC.G.6-Digital Citizen Security

Device-to-Device (D2D) communication is an important feature for many kinds of mobile networks and in particular for the UMTS Long Term Evolution Advanced (LTE-A) cellular technology, where an underlay network can be created between User Equipments (UEs) without the support of an evolved Node Bs (eNBs). The possibility to allow direct links between UEs plays an important role when the commercial LTE infrastructure is subject to failures or becomes unavailable after a disaster. Project ABSOLUTE, in scenarios for public safety, proposes raising in the sky a balloon with attached a 4G eNB (AeNB) to restore temporarily connectivity. In all cases in which UEs are out-ofcoverage of the AeNB like in indoors, D2D becomes crucial. In this work we propose to establish direct links employing a communication protocol that relies on the transmission of beacon frames sent in broadcast mode by selected UEs. We delve security aspects of this D2D protocol suitable for Public Safety (PS) users with out-of-coverage UEs based on sharing encryption keys. We pursue a network connectivity analysis of the secure protocol whereby we are able to show the existence of tradeoff points between connectivity and the increased overhead added by the security for different values of the system parameters., JRC.G.6-Digital Citizen Security