Single-board Device Individual Authentication based on Hardware Performance and Autoencoder Transformer Models

The proliferation of the Internet of Things (IoT) has led to the emergence of crowdsensing applications, where a multitude of interconnected devices collaboratively collect and analyze data. Ensuring the authenticity and integrity of the data collected by these devices is crucial for reliable decision-making and maintaining trust in the system. Traditional authentication methods are often vulnerable to attacks or can be easily duplicated, posing challenges to securing crowdsensing applications. Besides, current solutions leveraging device behavior are mostly focused on device identification, which is a simpler task than authentication. To address these issues, an individual IoT device authentication framework based on hardware behavior fingerprinting and Transformer autoencoders is proposed in this work. This solution leverages the inherent imperfections and variations in IoT device hardware to differentiate between devices with identical specifications. By monitoring and analyzing the behavior of key hardware components, such as the CPU, GPU, RAM, and Storage on devices, unique fingerprints for each device are created. The performance samples are considered as time series data and used to train outlier detection transformer models, one per device and aiming to model its normal data distribution. Then, the framework is validated within a spectrum crowdsensing system leveraging Raspberry Pi devices. After a pool of experiments, the model from each device is able to individually authenticate it between the 45 devices employed for validation. An average True Positive Rate (TPR) of 0.74+-0.13 and an average maximum False Positive Rate (FPR) of 0.06+-0.09 demonstrate the effectiveness of this approach in enhancing authentication, security, and trust in crowdsensing applications

Programas de investigación sobre propagación de llamas en condiciones de gravedad reducida

Se presenta en este trabajo un resumen del programa experimental sobre propagación de llamas a gravedad reducida que se está llevando a cabo por la Escuela Técnica Superior de Ingenieros Aeronáuticos; programa en el que últimamente colabora la empresa SENER. La propagación de llamas sobre la superficie de un sólido en atmósfera reactante es un tipo de proceso fuertemente afectado por la convección libre, y por tanto, especialmente sensible al valor de la gravedad

Beam Steering with Segmented Annular Arrays

Two-dimensional (2-D) arrays of squared matrix have maximum periodicity in their main directions; consequently, they require half wavelength (λ/2), interelement spacing to avoid grating lobes. This condition gives rise to well-known problems derived from the huge number of array elements and from their small size. In contrast, 2-D arrays with curvilinear configuration produce lower grating lobes and, therefore, allow the element size to be increased beyond λ/2. Using larger elements, these arrays have the advantage of reducing the number of elements and of increasing the signal-to-noise ratio (SNR). In this paper, the beamforming properties of segmented annular phased arrays are theoretically analyzed and compared with the equivalent squared matrix array. In the first part, point-like elements are considered in order to facilitate the field analysis with respect to the array structure. Afterward, the effect of the element size on the steered beam properties also is presented. In the examples, it is shown that the segmented annular array has notably lower grating lobes than the equivalent squared matrix array and that it is possible to design segmented annular arrays with interelement distance higher than λ whose beam characteristics are perfectly valid for volumetric imaging applications.This paper received the support of the Education and Science Ministry of Spain under its DPI2002-01583, DPI2004-06470, and DPI2004-06756 projects.Peer reviewe

Complementing the Pleistocene biogeography of European amphibians: Testimony from a southern Atlantic species

To reconstruct the historical biogeography of Hyla molleri, a tree frog endemic to the Eurosiberian and Mediterranean bioclimatic zones in the Iberian Peninsula. Location: Iberian Peninsul

Noise Sources, Effects and Countermeasures in Narrowband Power-Line Communications Networks: A Practical Approach

The integration of Distributed Generation, Electric Vehicles, and storage without compromising the quality of the power delivery requires the deployment of a communications overlay that allows monitoring and controlling low voltage networks in almost real time. Power Line Communications are gaining momentum for this purpose since they present a great trade-off between economic and technical features. However, the power lines also represent a harsh communications medium which presents different problems such as noise, which is indeed affected by Distributed Generation, Electric Vehicles, and storage. This paper provides a comprehensive overview of the types of noise that affects Narrowband Power Line Communications, including normative noises, noises coming from common electronic devices measured in actual operational power distribution networks, and noises coming from photovoltaic inverters and electric vehicle charging spots measured in a controlled environment. The paper also reviews several techniques to mitigate the effects of noise, paying special attention to passive filtering, as for being one of the most widely used solution to avoid this kind of problems in the field. In addition, the paper presents a set of tests carried out to evaluate the impact of some representative noises on Narrowband Power Line Communications network performance, as well as the effectiveness of different passive filter configurations to mitigate such an impact. In addition, the considered sources of noise can also bring value to further improve PLC communications in the new scenarios of the Smart Grid as an input to theoretical models or simulations.This work has been partly funded by the Spanish Ministry of Economy and Competitiveness through the National Program for Research Aimed at the Challenges of Society under the project OSIRIS (RTC-2014-1556-3) and through the network of excellence REDYD2050 (ENE2015-70032-REDT)

Noise Sources, Effects and Countermeasures in Narrowband Power-Line Communications Networks: A Practical Approach

The integration of Distributed Generation, Electric Vehicles, and storage without compromising the quality of the power delivery requires the deployment of a communications overlay that allows monitoring and controlling low voltage networks in almost real time. Power Line Communications are gaining momentum for this purpose since they present a great trade-off between economic and technical features. However, the power lines also represent a harsh communications medium which presents different problems such as noise, which is indeed affected by Distributed Generation, Electric Vehicles, and storage. This paper provides a comprehensive overview of the types of noise that affects Narrowband Power Line Communications, including normative noises, noises coming from common electronic devices measured in actual operational power distribution networks, and noises coming from photovoltaic inverters and electric vehicle charging spots measured in a controlled environment. The paper also reviews several techniques to mitigate the effects of noise, paying special attention to passive filtering, as for being one of the most widely used solution to avoid this kind of problems in the field. In addition, the paper presents a set of tests carried out to evaluate the impact of some representative noises on Narrowband Power Line Communications network performance, as well as the effectiveness of different passive filter configurations to mitigate such an impact. In addition, the considered sources of noise can also bring value to further improve PLC communications in the new scenarios of the Smart Grid as an input to theoretical models or simulations.This work has been partly funded by the Spanish Ministry of Economy and Competitiveness through the National Program for Research Aimed at the Challenges of Society under the project OSIRIS (RTC-2014-1556-3) and through the network of excellence REDYD2050 (ENE2015-70032-REDT)

CyberSpec: Intelligent Behavioral Fingerprinting to Detect Attacks on Crowdsensing Spectrum Sensors

Integrated sensing and communication (ISAC) is a novel paradigm using crowdsensing spectrum sensors to help with the management of spectrum scarcity. However, well-known vulnerabilities of resource-constrained spectrum sensors and the possibility of being manipulated by users with physical access complicate their protection against spectrum sensing data falsification (SSDF) attacks. Most recent literature suggests using behavioral fingerprinting and Machine/Deep Learning (ML/DL) for improving similar cybersecurity issues. Nevertheless, the applicability of these techniques in resource-constrained devices, the impact of attacks affecting spectrum data integrity, and the performance and scalability of models suitable for heterogeneous sensors types are still open challenges. To improve limitations, this work presents seven SSDF attacks affecting spectrum sensors and introduces CyberSpec, an ML/DL-oriented framework using device behavioral fingerprinting to detect anomalies produced by SSDF attacks affecting resource-constrained spectrum sensors. CyberSpec has been implemented and validated in ElectroSense, a real crowdsensing RF monitoring platform where several configurations of the proposed SSDF attacks have been executed in different sensors. A pool of experiments with different unsupervised ML/DL-based models has demonstrated the suitability of CyberSpec detecting the previous attacks within an acceptable timeframe