Interoperable Digital Proximity Tracing protocol (IDPT)

Draft Version 2, 19/05/2020, 25/05/2020This document introduces the Interoperable Digital Proximity Tracing (IDPT) protocol, that can be run by applications that also run the DP3T digital proximity tracking protocol to enable interoperability with the ROBERT digital proximity tracking protocol . We believe that the same mechanism can be adapted to allow interoperability between other decentralized and centralized digital proximity tracing protocols, but analysis of this is kept out of the scope of the document. The IDPT protocol avoids the reidentification attack of positive-tested users of the centralised system that was claimed to be an inherent property of interoperability systems , as in IDPT the system does not publish the list of decentralised ephemeral identifier that were at risk of exposure of users of app R. Moreover, it avoids the possibility of creation of proximity graphs for users of IDPT who were in contact with users of ROBERT. As is well known, the current iOS and Android Exposure Notification API only supports protocols of the distributed class. Due to this lack of support for centralized approaches, the implementation of the IDPT protocol has the same known difficulties as ROBERT, which appear mainly when applications are running in the background. Additionally, since devices must transmit more BLE beacons per second, we expect that devices running IDPT will have higher power consumption compared to implementing a pure DP3T mechanism. We believe that, in practice, the use of IDPT in countries where majority of users of DP3T-type applications should be optional, mainly in areas where the presence of R nodes is likely (for example, large cities, tourist areas, airports, etc.). Another situation in which the use of IDPT could be adequate is when a user of the app I visits a country where the majority of the population uses R application. In addition, a country could introduce a DP3T application in a first phase, and only later incorporate IDPT.Preprin

Sistema de detección de atacantes enmascarados basado en técnicas de alineamiento de secuencias

Los ataques enmascarados constituyen la actividad malintencionada perpetrada a partir de robos de identidad, entre la que se incluye la escalada de privilegios o el acceso no autorizados a activos del sistema. Este trabajo propone un sistema de detección de atacantes enmascarados mediante la observación de las secuencias de acciones llevadas a cabo por los usuarios legítimos del sistema. La clasificación de la actividad monitorizada es modelada y clasificada en base a algoritmos de alineamiento de secuencias locales. Para la validación del etiquetado se incorpora la prueba estadística no paramétrica de Mann-Whitney. Esto permite el análisis de secuencias en tiempo real. La experimentación realizada considera los conjuntos de muestras de Schonlau. La tasa de acierto al detectar ataques enmascarados es 98,3% y la tasa de falsos positivos es 0,77 %

Performance analysis of feedback-free collision resolution NDMA protocol

To support communications of a large number of deployed devices while guaranteeing limited signaling load, low energy consumption, and high reliability, future cellular systems require efficient random access protocols. However, how to address the collision resolution at the receiver is still the main bottleneck of these protocols. The network-assisted diversity multiple access (NDMA) protocol solves the issue and attains the highest potential throughput at the cost of keeping devices active to acquire feedback and repeating transmissions until successful decoding. In contrast, another potential approach is the feedback-free NDMA (FF-NDMA) protocol, in which devices do repeat packets in a pre-defined number of consecutive time slots without waiting for feedback associated with repetitions. Here, we investigate the FF-NDMA protocol from a cellular network perspective in order to elucidate under what circumstances this scheme is more energy efficient than NDMA. We characterize analytically the FF-NDMA protocol along with the multipacket reception model and a finite Markov chain. Analytic expressions for throughput, delay, capture probability, energy, and energy efficiency are derived. Then, clues for system design are established according to the different trade-offs studied. Simulation results show that FF-NDMA is more energy efficient than classical NDMA and HARQ-NDMA at low signal-to-noise ratio (SNR) and at medium SNR when the load increases.Peer ReviewedPostprint (published version

A comparative study of calibration methods for low-cost ozone sensors in IoT platforms

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper shows the result of the calibration process of an Internet of Things platform for the measurement of tropospheric ozone (O 3 ). This platform, formed by 60 nodes, deployed in Italy, Spain, and Austria, consisted of 140 metal–oxide O 3 sensors, 25 electro-chemical O 3 sensors, 25 electro-chemical NO 2 sensors, and 60 temperature and relative humidity sensors. As ozone is a seasonal pollutant, which appears in summer in Europe, the biggest challenge is to calibrate the sensors in a short period of time. In this paper, we compare four calibration methods in the presence of a large dataset for model training and we also study the impact of a limited training dataset on the long-range predictions. We show that the difficulty in calibrating these sensor technologies in a real deployment is mainly due to the bias produced by the different environmental conditions found in the prediction with respect to those found in the data training phase.Peer ReviewedPostprint (author's final draft

Modelos analíticos para la evaluación de mecanismos de control de tráfico en redes ATM

Se presentan una serie de modelos analíticos que son de utilidad para la evaluación de mecanismos de control de tráfico en redes digitales de servicios integrados de alta velocidad que usan conmutación de paquetes. En concreto se estudia el caso de redes ATM (Asynchronous Transfer Mode).Por un lado se ha desarrollado un modelo para evaluar la perturbación que se introduce al multiplexar un flujo individual de tasa constante que tráfico a ráfagas. Por otro lado, se ha estudiado mecanismos que permiten discriminar entre células ATM que ocupan un buffer de memoria en un mutiplexor o conmutador, estableciendo una prioridad de pérdidas.En el desarrollo de los modelos se ha tenido en cuenta las correlaciones que aparecen en los flujos de tráfico de dichas redes. Para ello se han usado modelos markovianos, en los que el estado de una cadena de Markov permite caracterizar la intensidad del tráfico. Los modelos de colas resultantes se han resuelto usando la metodología de análisis matricial de colas desarrollada principalmente por M. F. Neuts y modelos de aproximación de fluido.We study analytical models for the performance evaluation of traffic control mechanisms in high-speed packet switching digital integrated services networks. In particular, we focus our study on the case of ATM networks.The main contributions of this work are two: (i) An analytical model to evaluate the Cell Delay Variation introduced on a CBR flow, and (ii) several analytical models to study Space Priority mechanism, which introduce a priority among cells in the occupancy of multiplexers and switch buffers.These models take into account the traffic correlation present in this type of networks. We have used markovian models, where each state of the Markov chain characterizes the intensity of the traffic. The obtained queueing models where solved using the Matrix Analysis methodology developed by M. F. Neuts, and a fluid-flow approximation

On the Machinability of an Al-63%SiC Metal Matrix Composite

This paper presents a preliminary study of aluminium matrix composite materials during machining, with a special focus on their behavior under conventional processes. This work will expand the knowledge of these materials, which is considered to be strategic for some industrial sectors, such as the aeronautics, electronics, and automotive sectors. Finding a machining model will allow us to define the necessary parameters when applying the materials to industry. As a previous step of the material and its machining, an experimental state-of-the-art review has been carried out, revealing a lack of studies about the composition and material properties, processes, tools, and recommended parameters. The results obtained and reflected in this paper are as follows; SiC is present in metallic matrix composite (MMC) materials in a very wide variety of sizes. A metallographic study of the material confirms the high percentage of reinforcement and very high microhardness values registered. During the machining process, tools present a very high level of wear in a very short amount of time, where chips are generated and arcs are segmented, revealing the high microhardness of the material, which is given by its high concentration of SiC. The chip shape is the same among other materials with a similar microhardness, such as Ti or its alloys. The forces registered in the machining process are quite di erent from conventional alloys and are more similar to the values of harder alloys, which is also the case for chip generation. The results coincide, in part, with previous studies and also give new insight into the behavior of this material, which does not conform to the assumptions for standard metallic materials, where the hypothesis of Sha er is not directly applicable. On the other hand, here, cutting forces do not behave in accordance with the traditional model. This paper will contribute to improve the knowledge of the Al-63%SiC MMC itself and the machining behavior

LightDock: a new multi-scale approach to protein–protein docking

Computational prediction of protein–protein complex structure by docking can provide structural and mechanistic insights for protein interactions of biomedical interest. However, current methods struggle with difficult cases, such as those involving flexible proteins, low-affinity complexes or transient interactions. A major challenge is how to efficiently sample the structural and energetic landscape of the association at different resolution levels, given that each scoring function is often highly coupled to a specific type of search method. Thus, new methodologies capable of accommodating multi-scale conformational flexibility and scoring are strongly needed. We describe here a new multi-scale protein–protein docking methodology, LightDock, capable of accommodating conformational flexibility and a variety of scoring functions at different resolution levels. Implicit use of normal modes during the search and atomic/coarse-grained combined scoring functions yielded improved predictive results with respect to state-of-the-art rigid-body docking, especially in flexible cases.B.J-G was supported by a FPI fellowship from the Spanish Ministry of Economy and Competitiveness. This work was supported by I+D+I Research Project grants BIO2013-48213-R and BIO2016-79930-R from the Spanish Ministry of Economy and Competitiveness. This work is partially supported by the European Union H2020 program through HiPEAC (GA 687698), by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology (TIN2015-65316-P) and the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programaciói Entorns d’Execució Paral·lels (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

Two-level interacting boson models beyond the mean field

The phase diagram of two-level boson Hamiltonians, including the Interacting Boson Model (IBM), is studied beyond the standard mean field approximation using the Holstein-Primakoff mapping. The limitations of the usual intrinsic state (mean field) formalism concerning finite-size effects are pointed out. The analytic results are compared to numerics obtained from exact diagonalizations. Excitation energies and occupation numbers are studied in different model space regions (Casten triangle for IBM) and especially at the critical points.Comment: 14 pages, 13 figure

Sistema de deteccíón de anomalías de red basado en el procesamiento de la Carga Útil [Payload]

Los sistemas actuales de detección de anomalías basados en la carga útil pasan por serias dificultades a la hora de defenderse frente a ataques de tipo mimicry, así como ataques día cero, pudiendo poner en serio peligro los sistemas protegidos. El sistema propuesto en este documento, como preprocesador del IDS Snort, se basa en la correlación entre instrucciones de un mismo ataque para defenderse frente a ataques polimorficos, así como en los patrones de ataques ya conocidos, pudiendo así protegerse de ataques de reciente creación, dado que basan parte de su código en algún ataque conocido. Como método para conseguir estos objetivos se han evaluado diferentes vías que se desarrollan a lo largo de este documento. OpenMP nos proporciona paralelismo en arquitecturas de memoria compartida para acelerar el procesamiento de los paquetes, mientras que se han optimizado ciertas secciones críticas del proceamiento, así como del almacenamiento de las estructuras necesarias para almacenar la información generada. Se ha evaluado el rendimiento de la nueva implementación con tráfico real proveniente de la red de la UCM, dichos resultados arrojan interesantes observaciones sobre el algoritmo. Como líneas de investigación en progreso quedaría transformar las secciones críticas del procesamiento a GPGPU, ya sea CUDA u OpenCL, así como el uso de sistemas de correlación de alertas para descargar de trabajo al IDS. [ABSTRACT] Nowadays payload anomaly based detection systems go through serious difficulties when facing mimicry type attacks, as well as zero day attacks, putting protected systems on jeopardy. The system proposed on this document, as a Snort preprocessor, is based on attack instructions correlation to defend against polymorphic attacks, aditionally the use of well known attack patterns allows us to protect the network against new attacks, since a part of their code relies on already known attacks. Different ways of developement have been evaluated when pursuing these goals, being all of them presented troughout this document. While OpenMP provides us with enhaced performance on the processing of packages by using shared memory parallelism, critical sections of the processing algorithm have been improved, as well as the storage of the necessary data structures to store all of the generated information. Performance of the new implementation has been tested with real traffic from the UCM net, these results show up interesting observations about the algorithm. As current progress research lines it is important to highlight the implementation on GPGPU, CUDA or OpenCL, of critical parts of the processing algorithm, as well as the use of alert correlation systems to relieve the IDS of a part of its workload

Graph signal reconstruction techniques for IoT air pollution monitoring platforms

Air pollution monitoring platforms play a very important role in preventing and mitigating the effects of pollution. Recent advances in the field of graph signal processing have made it possible to describe and analyze air pollution monitoring networks using graphs. One of the main applications is the reconstruction of the measured signal in a graph using a subset of sensors. Reconstructing the signal using information from neighboring sensors is a key technique for maintaining network data quality, with examples including filling in missing data with correlated neighboring nodes, creating virtual sensors, or correcting a drifting sensor with neighboring sensors that are more accurate. This paper proposes a signal reconstruction framework for air pollution monitoring data where a graph signal reconstruction model is superimposed on a graph learned from the data. Different graph signal reconstruction methods are compared on actual air pollution data sets measuring O3, NO2, and PM10. The ability of the methods to reconstruct the signal of a pollutant is shown, as well as the computational cost of this reconstruction. The results indicate the superiority of methods based on kernel-based graph signal reconstruction, as well as the difficulties of the methods to scale in an air pollution monitoring network with a large number of low-cost sensors. However, we show that the scalability of the framework can be improved with simple methods, such as partitioning the network using a clustering algorithm.This work is supported by the National Spanish funding PID2019-107910RB-I00, by regional project 2017SGR-990, and with the support of Secretaria d’Universitats i Recerca de la Generalitat de Catalunya i del Fons Social Europeu.Peer ReviewedPostprint (author's final draft