Towards Adaptive RF Fingerprint-based Authentication of IIoT devices

As IoT technologies mature, they are increasingly finding their way into more sensitive domains, such as Medical and Industrial IoT, in which safety and cyber-security are of great importance. While the number of deployed IoT devices continues to increase exponentially, they still present severe cyber-security vulnerabilities. Effective authentication is paramount to support trustworthy IIoT communications, however, current solutions focus on upper-layer identity verification or key-based cryptography which are often inadequate to the heterogeneous IIoT environment. In this work, we present a first step towards achieving powerful and flexible IIoT device authentication, by leveraging AI adaptive Radio Frequency Fingerprinting technique selection and tuning, at the PHY layer for highly accurate device authentication over challenging RF environments

A methodology for the resolution of cashtag collisions on Twitter – A natural language processing & data fusion approach

Investors utilise social media such as Twitter as a means of sharing news surrounding financials stocks listed on international stock exchanges. Company ticker symbols are used to uniquely identify companies listed on stock exchanges and can be embedded within tweets to create clickable hyperlinks referred to as cashtags, allowing investors to associate their tweets with specific companies. The main limitation is that identical ticker symbols are present on exchanges all over the world, and when searching for such cashtags on Twitter, a stream of tweets is returned which match any company in which the cashtag refers to - we refer to this as a cashtag collision. The presence of colliding cashtags could sow confusion for investors seeking news regarding a specific company. A resolution to this issue would benefit investors who rely on the speediness of tweets for financial information, saving them precious time. We propose a methodology to resolve this problem which combines Natural Language Processing and Data Fusion to construct company-specific corpora to aid in the detection and resolution of colliding cashtags, so that tweets can be classified as being related to a specific stock exchange or not. Supervised machine learning classifiers are trained twice on each tweet – once on a count vectorisation of the tweet text, and again with the assistance of features contained in the company-specific corpora. We validate the cashtag collision methodology by carrying out an experiment involving companies listed on the London Stock Exchange. Results show that several machine learning classifiers benefit from the use of the custom corpora, yielding higher classification accuracy in the prediction and resolution of colliding cashtags

Análisis Urbano y Comunidades Inteligentes: Una Aproximación al Empleo de la Tecnología en la Movilidad Cotidiana

Concentration of population in urban centers is a global problem for which different strategies in order to organize different processes in cities and improve the quality of life are required. The creation of smart communities is shown as a sustainable solution since they deal with various key aspects, such as traffic management and mobility, through the use of information technologies (ITs). This work presents a review of recent studies using information technologies for urban analysis and mobility in cities. A descriptive analysis of automated methods for collecting and analyzing citizens’ mobility patterns is performed; it is centered in smart card use, geolocation and geotagging. It is concluded that a robust communication infrastructure, supported by an efficient computational platform allowing big data management and ubiquitous computing, is a crucial aspect for urban management in a smart communityLa concentración de la población en los centros urbanos es una problemática mundial que requiere de estrategias que permitan organizar sus procesos y mejorar la calidad de vida. La creación de comunidades inteligentes se muestra como una solución sostenible, debido a que éstas trabajan aspectos claves para el desarrollo urbano, como la gestión de tráfico y la movilidad, apoyada en las tecnologías de la información (TICs). Este trabajo presenta una revisión del estado del arte en cuanto a la aplicación de las TICs al análisis urbano y movilidad ciudadana. Se analizan descriptivamente diversos métodos automáticos para la recolección y el análisis del patrón de movilidad de los ciudadanos, enfocándose en el uso de tarjetas inteligentes, geolocalización y geoetiquetado. Se encuentra que una infraestructura de comunicaciones robusta, apoyada en una plataforma computacional ágil con manejo de grandes datos y computación ubicua, es primordial para la gestión urbana en una comunidad inteligente

Análisis urbano y comunidades inteligentes: “una aproximación al empleo de la tecnología en la movilidad cotidiana”

Concentration of population in urban centers is a global problem for which different strategies in order to organize different processes in cities and improve the quality of life are required. The creation of smart communities is shown as a sustainable solution since they deal with various key aspects, such as traffc management and mobility, through the use of information technologies (ITs). This work presents a review of recent studies using information technologies for urban analysis and mobility in cities. A descriptive analysis of automated methods for collecting and analyzing citizens’ mobility patterns is performed; it is centered in smart card use, geolocation and geotagging. It is concluded that a robust communication infrastructure, supported by an effcient computational platform allowing big data management and ubiquitous computing, is a crucial aspect for urban management in a smart community.La concentración de la población en los centros urbanos es una problemática mundial que requiere de estrategias que permitan organizar sus procesos y mejorar la calidad de vida. La creación de comunidades inteligentes se muestra como una solución sostenible, debido a que éstas trabajan aspectos claves para el desarrollo urbano, como la gestión de tráfco y la movilidad, apoyada en las tecnologías de la información (TICs). Este trabajo presenta una revisión del estado del arte en cuanto a la aplicación de las TICs al análisis urbano y movilidad ciudadana. Se analizan descriptivamente diversos métodos automáticos para la recolección y el análisis del patrón de movilidad de los ciudadanos, enfocándose en el uso de tarjetas inteligentes, geolocalización y geoetiquetado. Se encuentra que una infraestructura de comunicaciones robusta, apoyada en una plataforma computacional ágil con manejo de grandes datos y computación ubicua, es primordial para la gestión urbana en una comunidad inteligente

Decentralized and collaborative machine learning framework for IoT

Decentralized machine learning has recently been proposed as a potential solution to the security issues of the canonical federated learning approach. In this paper, we propose a decentralized and collaborative machine learning framework specially oriented to resource-constrained devices, usual in IoT deployments. With this aim we propose the following construction blocks. First, an incremental learning algorithm based on prototypes that was specifically implemented to work in low-performance computing elements. Second, two random-based protocols to exchange the local models among the computing elements in the network. Finally, two algorithmics approaches for prediction and prototype creation. This proposal was compared to a typical centralized incremental learning approach in terms of accuracy, training time and robustness with very promising results.Axencia Galega de Innovación | Ref. 25/IN606D/2021/2612348Agencia Estatal de Investigación | Ref. PID2020-113795RB-C3

Analysis of the NS-3 module QKDNetSim for the Simulation of QKD Networks

Cursos e Congresos, C-155[Abstract] Quantum Key Distribution (QKD) is a promising technology that allows two nodes to privately agree on a key through a quantum channel. Unfortunately, QKD is still in experimental phase and researchers must rely on simulators to replicate the behaviour of a quantum network. One of the most widespread is QKDNetSim, a module for the C++ network simulator NS-3. However, this module is very limited in its behaviour, so it does not faithfully represent a real quantum network. In this work we analyse the structure and components of QKDNetSim, as well as its shortcomings and how they affect the quality of the simulationThis work is part of the project TED2021-130369B-C31, TED2021-130369BC32, TED2021-130369B-C33 and TED2021-130492B-C21 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. The work is also funded by the Plan Complementario de Comunicaciones Cuánticas, Spanish Ministry of Science and Innovation (MICINN), Plan de Recuperación NextGenerationEU de la Unión Europea (PRTR-C17.I1, CITIC Ref. 305.2022), and Regional Government of Galicia (Agencia Gallega de Innovación, GAIN, CITIC Ref. 306.2022

Using Decentralized Aggregation for Federated Learning with Differential Privacy

Nowadays, the ubiquitous usage of mobile devices and networks have raised concerns about the loss of control over personal data and research advance towards the trade-off between privacy and utility in scenarios that combine exchange communications, big databases and distributed and collaborative (P2P) Machine Learning techniques. On the other hand, although Federated Learning (FL) provides some level of privacy by retaining the data at the local node, which executes a local training to enrich a global model, this scenario is still susceptible to privacy breaches as membership inference attacks. To provide a stronger level of privacy, this research deploys an experimental environment for FL with Differential Privacy (DP) using benchmark datasets. The obtained results show that the election of parameters and techniques of DP is central in the aforementioned trade-off between privacy and utility by means of a classification example

Towards predictive models in food engineering: Parameter estimation dos and don'ts

1 póster.-- 29th EFFoST International Conference, 10-12 November 2015, Athens, GreeceRigorous, physics based, modeling is at the core of computer aided food process engineering. Models often require the values of some, typically unknown, parameters (thermo-physical properties, kinetic constants, etc). Therefore, parameter estimation from experimental data is critical to achieve desired model predictive properties. Unfortunately, it must be admitted that often experiment design and modeling are fully separated tasks: experiments are not designed for the purpose of modeling and models are usually derived without paying especial attention to available experimental data or experimentation capabilities. When, at some point, the parameter estimation problem is put on the table, modelers use available experimental data to ``manually'' tune the unknown parameters. This results in inaccurate parameter estimates, usually experiment dependent, with the implications this has in model validation. This work takes a new look into the parameter estimation problem in food process modeling. First the common pitfalls in parameter estimation are described. Second we present the theoretical background and the numerical techniques to define a parameter estimation protocol to iteratively improve model predictive capabilities. This protocol includes: reduced order modeling, structural and practical identifiability analyses, data fitting with global optimization methods and optimal experimental design. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged foods. The model was experimentally validated in the IIM-CSIC pilot plantThe authors acknowledge financial support from the EU (Project SPECTRAFISH), Spanish Ministry of Science and Innovation (Project ISFORQUALITY) and CSIC (Project CONTROLA)Peer reviewe