118 research outputs found
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 Review on Cryptocurrency Transaction Methods for Money Laundering
Cryptocurrencies are considered relevant assets and they are currently used
as an investment or to carry out transactions. However, specific
characteristics commonly associated with the cryptocurrencies such as
irreversibility, immutability, decentralized architecture, absence of control
authority, mobility, and pseudo-anonymity make them appealing for money
laundering activities. Thus, the collection and characterization of current
cryptocurrency-based methods used for money laundering are paramount to
understanding the circulation flows of physical and digital money and
preventing this illegal activity. In this paper, a collection of cryptocurrency
transaction methods is presented and distributed through the money laundering
life cycle. Each method is analyzed and classified according to the phase of
money laundering it corresponds to. The result of this article may in the
future help design efficient strategies to prevent illegal money laundering
activities
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
- …