Intentions: a confident-based interaction design for smart spaces

The paradigm of ubiquitous computing has become a reference for the design of Smart Spaces. Current trends in Ambient Intelligence are increasingly related to the scope of Internet of Things. This paradigm has the potential to support cost-effective solutions in the fields of telecare, e-health and Ambient Assisted Living. Nevertheless, ubiquitous computing does not provide end users with a role for proactive interactions with the environment. Thus, the deployment of smart health care services at a private space like the home is still unsolved. This PhD dissertation aims to define a person-environment interaction model to foster acceptability and users confidence in private spaces by applying the concept of user-centred security and the human performance model of seven stages of action.

An Architecture forRepresenting Biological Processes based on Networks of Bio-inspired Processors

n this paper we propose the use of Networks of Bio-inspired Processors (NBP) to model some biological phenomena within a computational framework. In particular, we propose the use of an extension of NBP named Network Evolutionary Processors Transducers to simulate chemical transformations of substances. Within a biological process, chemical transformations of substances are basic operations in the change of the state of the cell. Previously, it has been proved that NBP are computationally complete, that is, they are able to solve NP complete problems in linear time, using massively parallel computations. In addition, we propose a multilayer architecture that will allow us to design models of biological processes related to cellular communication as well as their implications in the metabolic pathways. Subsequently, these models can be applied not only to biological-cellular instances but, possibly, also to configure instances of interactive processes in many other fields like population interactions, ecological trophic networks, in dustrial ecosystems, etc.

STONE: a stream-based DDoS defense framework

An effective Distributed Denial of Service (DDoS) defense mechanism must guarantee legitimate users access to an Internet service masking the effects of possible attacks. That is, it must be able to detect threats and discard malicious packets in a online fashion. Given that emerging data streaming technology can enable such mitigation in an effective manner, in this paper we present STONE, a stream-based DDoS defense framework, which integrates anomaly-based DDoS detection and mitigation with scalable data streaming technology. With STONE, the traffic of potential targets is analyzed via continuous data streaming queries maintaining information used for both attack detection and mitigation. STONE provides minimal degradation of legitimate users traffic during DDoS attacks and it also faces effectively flash crowds. Our preliminary evaluation based on an implemented prototype and conducted with real legitimate and malicious traffic traces shows that STONE is able to provide fast detection and precise mitigation of DDoS attacks leveraging scalable data streaming technology.

Accuracy estimates based on multifield variational principles

Recent developments to fit the so called Free Formulation into a variational framework have suggested the possibility of introducing a new category of error estimates for finite element computations. Such error estimates are based on differences between certain multifield functionals, which give the same value for the true solution. In the present paper the formulation of some estimates of this kind is introduced for elasticity and plate bending problems, and several examples of their performance are discussed. The observed numerical behavior of the new accuracy measures seems to be acceptable from an engineering point of view. However, further numerical experimentation is still needed to establish practical tolerance levels for real problems.

Análisis y modelización de la dinámica emergente durante el proceso de difusión de información en las redes sociales de internet

Durante la actividad diaria, la sociedad actual interactúa constantemente por medio de dispositivos electrónicos y servicios de telecomunicaciones, tales como el teléfono, correo electrónico, transacciones bancarias o redes sociales de Internet. Sin saberlo, masivamente dejamos rastros de nuestra actividad en las bases de datos de empresas proveedoras de servicios. Estas nuevas fuentes de datos tienen las dimensiones necesarias para que se puedan observar patrones de comportamiento humano a grandes escalas. Como resultado, ha surgido una reciente explosión sin precedentes de estudios de sistemas sociales, dirigidos por el análisis de datos y procesos computacionales. En esta tesis desarrollamos métodos computacionales y matemáticos para analizar sistemas sociales por medio del estudio combinado de datos derivados de la actividad humana y la teoría de redes complejas. Nuestro objetivo es caracterizar y entender los sistemas emergentes de interacciones sociales en los nuevos espacios tecnológicos, tales como la red social Twitter y la telefonía móvil. Analizamos los sistemas por medio de la construcción de redes complejas y series temporales, estudiando su estructura, funcionamiento y evolución en el tiempo. También, investigamos la naturaleza de los patrones observados por medio de los mecanismos que rigen las interacciones entre individuos, así como medimos el impacto de eventos críticos en el comportamiento del sistema. Para ello, hemos propuesto modelos que explican las estructuras globales y la dinámica emergente con que fluye la información en el sistema. Para los estudios de la red social Twitter, hemos basado nuestros análisis en conversaciones puntuales, tales como protestas políticas, grandes acontecimientos o procesos electorales. A partir de los mensajes de las conversaciones, identificamos a los usuarios que participan y construimos redes de interacciones entre los mismos. Específicamente, construimos una red para representar quién recibe los mensajes de quién y otra red para representar quién propaga los mensajes de quién. En general, hemos encontrado que estas estructuras tienen propiedades complejas, tales como crecimiento explosivo y distribuciones de grado libres de escala. En base a la topología de estas redes, hemos indentificado tres tipos de usuarios que determinan el flujo de información según su actividad e influencia. Para medir la influencia de los usuarios en las conversaciones, hemos introducido una nueva medida llamada eficiencia de usuario. La eficiencia se define como el número de retransmisiones obtenidas por mensaje enviado, y mide los efectos que tienen los esfuerzos individuales sobre la reacción colectiva. Hemos observado que la distribución de esta propiedad es ubicua en varias conversaciones de Twitter, sin importar sus dimensiones ni contextos. Con lo cual, sugerimos que existe universalidad en la relación entre esfuerzos individuales y reacciones colectivas en Twitter. Para explicar los factores que determinan la emergencia de la distribución de eficiencia, hemos desarrollado un modelo computacional que simula la propagación de mensajes en la red social de Twitter, basado en el mecanismo de cascadas independientes. Este modelo nos permite medir el efecto que tienen sobre la distribución de eficiencia, tanto la topología de la red social subyacente, como la forma en que los usuarios envían mensajes. Los resultados indican que la emergencia de un grupo selecto de usuarios altamente eficientes depende de la heterogeneidad de la red subyacente y no del comportamiento individual. Por otro lado, hemos desarrollado técnicas para inferir el grado de polarización política en redes sociales. Proponemos una metodología para estimar opiniones en redes sociales y medir el grado de polarización en las opiniones obtenidas. Hemos diseñado un modelo donde estudiamos el efecto que tiene la opinión de un pequeño grupo de usuarios influyentes, llamado élite, sobre las opiniones de la mayoría de usuarios. El modelo da como resultado una distribución de opiniones sobre la cual medimos el grado de polarización. Aplicamos nuestra metodología para medir la polarización en redes de difusión de mensajes, durante una conversación en Twitter de una sociedad políticamente polarizada. Los resultados obtenidos presentan una alta correspondencia con los datos offline. Con este estudio, hemos demostrado que la metodología propuesta es capaz de determinar diferentes grados de polarización dependiendo de la estructura de la red. Finalmente, hemos estudiado el comportamiento humano a partir de datos de telefonía móvil. Por una parte, hemos caracterizado el impacto que tienen desastres naturales, como innundaciones, sobre el comportamiento colectivo. Encontramos que los patrones de comunicación se alteran de forma abrupta en las áreas afectadas por la catástofre. Con lo cual, demostramos que se podría medir el impacto en la región casi en tiempo real y sin necesidad de desplegar esfuerzos en el terreno. Por otra parte, hemos estudiado los patrones de actividad y movilidad humana para caracterizar las interacciones entre regiones de un país en desarrollo. Encontramos que las redes de llamadas y trayectorias humanas tienen estructuras de comunidades asociadas a regiones y centros urbanos. En resumen, hemos mostrado que es posible entender procesos sociales complejos por medio del análisis de datos de actividad humana y la teoría de redes complejas. A lo largo de la tesis, hemos comprobado que fenómenos sociales como la influencia, polarización política o reacción a eventos críticos quedan reflejados en los patrones estructurales y dinámicos que presentan la redes construidas a partir de datos de conversaciones en redes sociales de Internet o telefonía móvil. ABSTRACT During daily routines, we are constantly interacting with electronic devices and telecommunication services. Unconsciously, we are massively leaving traces of our activity in the service providers’ databases. These new data sources have the dimensions required to enable the observation of human behavioral patterns at large scales. As a result, there has been an unprecedented explosion of data-driven social research. In this thesis, we develop computational and mathematical methods to analyze social systems by means of the combined study of human activity data and the theory of complex networks. Our goal is to characterize and understand the emergent systems from human interactions on the new technological spaces, such as the online social network Twitter and mobile phones. We analyze systems by means of the construction of complex networks and temporal series, studying their structure, functioning and temporal evolution. We also investigate on the nature of the observed patterns, by means of the mechanisms that rule the interactions among individuals, as well as on the impact of critical events on the system’s behavior. For this purpose, we have proposed models that explain the global structures and the emergent dynamics of information flow in the system. In the studies of the online social network Twitter, we have based our analysis on specific conversations, such as political protests, important announcements and electoral processes. From the messages related to the conversations, we identify the participant users and build networks of interactions with them. We specifically build one network to represent whoreceives- whose-messages and another to represent who-propagates-whose-messages. In general, we have found that these structures have complex properties, such as explosive growth and scale-free degree distributions. Based on the topological properties of these networks, we have identified three types of user behavior that determine the information flow dynamics due to their influence. In order to measure the users’ influence on the conversations, we have introduced a new measure called user efficiency. It is defined as the number of retransmissions obtained by message posted, and it measures the effects of the individual activity on the collective reacixtions. We have observed that the probability distribution of this property is ubiquitous across several Twitter conversation, regardlessly of their dimension or social context. Therefore, we suggest that there is a universal behavior in the relationship between individual efforts and collective reactions on Twitter. In order to explain the different factors that determine the user efficiency distribution, we have developed a computational model to simulate the diffusion of messages on Twitter, based on the mechanism of independent cascades. This model, allows us to measure the impact on the emergent efficiency distribution of the underlying network topology, as well as the way that users post messages. The results indicate that the emergence of an exclusive group of highly efficient users depends upon the heterogeneity of the underlying network instead of the individual behavior. Moreover, we have also developed techniques to infer the degree of polarization in social networks. We propose a methodology to estimate opinions in social networks and to measure the degree of polarization in the obtained opinions. We have designed a model to study the effects of the opinions of a small group of influential users, called elite, on the opinions of the majority of users. The model results in an opinions distribution to which we measure the degree of polarization. We apply our methodology to measure the polarization on graphs from the messages diffusion process, during a conversation on Twitter from a polarized society. The results are in very good agreement with offline and contextual data. With this study, we have shown that our methodology is capable of detecting several degrees of polarization depending on the structure of the networks. Finally, we have also inferred the human behavior from mobile phones’ data. On the one hand, we have characterized the impact of natural disasters, like flooding, on the collective behavior. We found that the communication patterns are abruptly altered in the areas affected by the catastrophe. Therefore, we demonstrate that we could measure the impact of the disaster on the region, almost in real-time and without needing to deploy further efforts. On the other hand, we have studied human activity and mobility patterns in order to characterize regional interactions on a developing country. We found that the calls and trajectories networks present community structure associated to regional and urban areas. In summary, we have shown that it is possible to understand complex social processes by means of analyzing human activity data and the theory of complex networks. Along the thesis, we have demonstrated that social phenomena, like influence, polarization and reaction to critical events, are reflected in the structural and dynamical patterns of the networks constructed from data regarding conversations on online social networks and mobile phones.

Flammability conditions for ultra-lean hydrogen premixed combustion based on flame-ball analyses

It has been reasoned that the structures of strongly cellular flames in very lean mixtures approach an array of flame balls, each burning as if it were isolated, thereby indicating a connection between the critical conditions required for existence of steady flame balls and those necessary for occurrence of self-sustained premixed combustion. This is the starting assumption of the present study, in which structures of near-limit steady sphericosym-metrical flame balls are investigated with the objective of providing analytic expressions for critical combustion conditions in ultra-lean hydrogen-oxygen mixtures diluted with N2 and water vapor. If attention were restricted to planar premixed flames, then the lean-limit mole fraction of H2 would be found to be roughly ten percent, more than twice the observed flammability limits, thereby emphasizing the relevance of the flame-ball phenomena. Numerical integrations using detailed models for chemistry and radiation show that a onestep chemical-kinetic reduced mechanism based on steady-state assumptions for all chemical intermediates, together with a simple, optically thin approximation for water-vapor radiation, can be used to compute near-limit fuel-lean flame balls with excellent accuracy. The previously developed one-step reaction rate includes a crossover temperature that determines in the first approximation a chemical-kinetic lean limit below which combustión cannot occur, with critical conditions achieved when the diffusion-controlled radiation-free peak temperature, computed with account taken of hydrogen Soret diffusion, is equal to the crossover temperature. First-order corrections are found by activation-energy asymptotics in a solution that involves a near-field radiation-free zone surrounding a spherical flame sheet, together with a far-field radiation-conduction balance for the temperature profile. Different scalings are found depending on whether or not the surrounding atmosphere contains wáter vapor, leading to different analytic expressions for the critical conditions for flame-ball existence, which give results in very good agreement with those obtained by detailed numerical computations.

A survey of feature selection in Internet traffic characterization

In the last decade, the research community has focused on new classification methods that rely on statistical characteristics of Internet traffic, instead of pre-viously popular port-number-based or payload-based methods, which are under even bigger constrictions. Some research works based on statistical characteristics generated large fea-ture sets of Internet traffic; however, nowadays it?s impossible to handle hun-dreds of features in big data scenarios, only leading to unacceptable processing time and misleading classification results due to redundant and correlative data. As a consequence, a feature selection procedure is essential in the process of Internet traffic characterization. In this paper a survey of feature selection methods is presented: feature selection frameworks are introduced, and differ-ent categories of methods are briefly explained and compared; several proposals on feature selection in Internet traffic characterization are shown; finally, future application of feature selection to a concrete project is proposed.

Evaluating a web-based clinical decision support system for language disorders screening in a nursery school

Background: Early and effective identification of developmental disorders during childhood remains a critical task for the international community. The second highest prevalence of common developmental disorders in children are language delays, which are frequently the first symptoms of a possible disorder. Objective: This paper evaluates a Web-based Clinical Decision Support System (CDSS) whose aim is to enhance the screening of language disorders at a nursery school. The common lack of early diagnosis of language disorders led us to deploy an easy-to-use CDSS in order to evaluate its accuracy in early detection of language pathologies. This CDSS can be used by pediatricians to support the screening of language disorders in primary care. Methods: This paper details the evaluation results of the ?Gades? CDSS at a nursery school with 146 children, 12 educators, and 1 language therapist. The methodology embraces two consecutive phases. The first stage involves the observation of each child?s language abilities, carried out by the educators, to facilitate the evaluation of language acquisition level performed by a language therapist. Next, the same language therapist evaluates the reliability of the observed results. Results: The Gades CDSS was integrated to provide the language therapist with the required clinical information. The validation process showed a global 83.6% (122/146) success rate in language evaluation and a 7% (7/94) rate of non-accepted system decisions within the range of children from 0 to 3 years old. The system helped language therapists to identify new children with potential disorders who required further evaluation. This process will revalidate the CDSS output and allow the enhancement of early detection of language disorders in children. The system does need minor refinement, since the therapists disagreed with some questions from the CDSS knowledge base (KB) and suggested adding a few questions about speech production and pragmatic abilities. The refinement of the KB will address these issues and include the requested improvements, with the support of the experts who took part in the original KB development. Conclusions: This research demonstrated the benefit of a Web-based CDSS to monitor children?s neurodevelopment via the early detection of language delays at a nursery school. Current next steps focus on the design of a model that includes pseudo auto-learning capacity, supervised by experts.

A component- and connector-based approach for end-user composite web applications development

Enabling real end-user development is the next logical stage in the evolution of Internet-wide service-based applications. Successful composite applications rely on heavyweight service orchestration technologies that raise the bar far above end-user skills. This weakness can be attributed to the fact that the composition model does not satisfy end-user needs rather than to the actual infrastructure technologies. In our opinion, the best way to overcome this weakness is to offer end-to-end composition from the user interface to service invocation, plus an understandable abstraction of building blocks and a visual composition technique empowering end users to develop their own applications. In this paper, we present a visual framework for end users, called FAST, which fulfils this objective. FAST implements a novel composition model designed to empower non-programmer end users to create and share their own self-service composite applications in a fully visual fashion. We projected the development environment implementing this model as part of the European FP7 FAST Project, which was used to validate the rationale behind our approach.

Automated end user-centred adaptation of web components through automated description logic-based reasoning

Context: This paper addresses one of the major end-user development (EUD) challenges, namely, how to pack today?s EUD support tools with composable elements. This would give end users better access to more components which they can use to build a solution tailored to their own needs. The success of later end-user software engineering (EUSE) activities largely depends on how many components each tool has and how adaptable components are to multiple problem domains. Objective: A system for automatically adapting heterogeneous components to a common development environment would offer a sizeable saving of time and resources within the EUD support tool construction process. This paper presents an automated adaptation system for transforming EUD components to a standard format. Method: This system is based on the use of description logic. Based on a generic UML2 data model, this description logic is able to check whether an end-user component can be transformed to this modeling language through subsumption or as an instance of the UML2 model. Besides it automatically finds a consistent, non-ambiguous and finite set of XSLT mappings to automatically prepare data in order to leverage the component as part of a tool that conforms to the target UML2 component model. Results: The proposed system has been successfully applied to components from four prominent EUD tools. These components were automatically converted to a standard format. In order to validate the proposed system, rich internet applications (RIA) used as an operational support system for operators at a large services company were developed using automatically adapted standard format components. These RIAs would be impossible to develop using each EUD tool separately. Conclusion: The positive results of applying our system for automatically adapting components from current tool catalogues are indicative of the system?s effectiveness. Use of this system could foster the growth of web EUD component catalogues, leveraging a vast ecosystem of user-centred SaaS to further current EUSE trends.

Servicios de redes sociales en internet para generación de consenso

En los últimos años, la cantidad de información que se ha generado en la web ha sido enorme. El tráfico que se está generando en la actualidad es de unos 16144 Gigabytes/s, pero se estima que para el 2019 será tres veces mayor, de unos 51794 Gigabytes/s (Cisco Systems, Inc, 2015). Esta ingente cantidad de tráfico viene sobre todo de vídeo, audio e imágenes, ya que estos son los contenidos que requieren más almacenamiento. En esta era de la información, si nos paramos a pensar por un segundo, nos podemos dar cuenta que donde realmente está esa información es en el texto. En la web hay muchísimo texto, pero no supone una gran cantidad de almacenamiento. Esto no quiere decir que lo podamos despreciar. Lo que tenemos que hacer es darle relevancia a todo ese texto, y hacer énfasis en lo importante. Esto es lo que podríamos hacer con herramientas de argumentación. La argumentación se refiere al estudio de las disconformidades, de los diálogos y los textos que usamos para intentar convencer a alguien de nuestro punto de vista. Por tanto, estas herramientas deberían de ser capaces de organizar un diálogo o discusión y obtener un resultado de esa conversación. Esto es muy normal en las discusiones habladas: discutimos para llegar a un acuerdo (o no), pero no es tan común en las discusiones online. En las discusiones online suele haber muchísimos comentarios, sin orden alguno, y sin conocer cuál es el acuerdo al que se ha llegado. Este trabajo hará una presentación de la argumentación y los modelos de argumentación: esquemas o estructuras a los cuales responde una discusión o un texto argumentativo. Así pues, nos centramos en las herramientas disponibles, ya que queremos conocer en qué estado se encuentran: si realmente ya hay herramientas que solucionen el problema organizativo o no. Por tanto, se presentan varias herramientas y se comparan en base al problema que existe utilizando ciertas características necesarias para el correcto funcionamiento de una de estas herramientas. Es ahí donde conoceremos si la herramienta que buscamos existe.---ABSTRACT---In recent years, the amount of Information generated on the web has been quite great. The traffic generated currently is approximately 16144 Gigabytes/s, but it is estimated that it will be three times larger by 2019, approximately 51794 Gigabytes/s (Cisco Systems, Inc, 2015). This huge amount of traffic comes mostly from multimedia (video, audio and images), as these kind of contents require more storage. This is the information era. If we think a bit about it, we can realize that this information is actually in the text. The web contains so much text, but it represents a small part of the whole storage. This does not mean that we can underestimate it. What we have to do is to give it relevance and emphasize what is important. This could be done by using argumentation tools. Argumentation refers to the study of agreement, disagreement, and of the dialogues and writing through which we convince others of our points of view. Therefore, these tools should be able to organize a dialogue or discussion and obtain from it an outcome or answer. It is common to obtain this outcome in spoken discussions: we discuss to reach an agreement (or not), however it is not really common in online discussions. In online discussions, they tend to have a huge amount of comments, with no order, and without knowing the outcome that has been reached. This thesis will explain the argumentation and the argumentation models: schemes or structures which a discussion or argumentative text respond to. Knowing this, we will focus on the available tools, because we want to know their state: whether there already are tools which can solve the organizational problem or not. Therefore, several tools are presented and compared based on this problem using some features that are needed for the proper functioning of these tools. This is where we will know whether the tool we are looking for exists or not.

Self-adaptive strategy based on fuzzy control systems for improving performance in wireless sensors networks

The solutions to cope with new challenges that societies have to face nowadays involve providing smarter daily systems. To achieve this, technology has to evolve and leverage physical systems automatic interactions, with less human intervention. Technological paradigms like Internet of Things (IoT) and Cyber-Physical Systems (CPS) are providing reference models, architectures, approaches and tools that are to support cross-domain solutions. Thus, CPS based solutions will be applied in different application domains like e-Health, Smart Grid, Smart Transportation and so on, to assure the expected response from a complex system that relies on the smooth interaction and cooperation of diverse networked physical systems. The Wireless Sensors Networks (WSN) are a well-known wireless technology that are part of large CPS. The WSN aims at monitoring a physical system, object, (e.g., the environmental condition of a cargo container), and relaying data to the targeted processing element. The WSN communication reliability, as well as a restrained energy consumption, are expected features in a WSN. This paper shows the results obtained in a real WSN deployment, based on SunSPOT nodes, which carries out a fuzzy based control strategy to improve energy consumption while keeping communication reliability and computational resources usage among boundaries.

Clasificación de tráfico de Internet mediante análisis de datos

Poder clasificar de manera precisa la aplicación o programa del que provienen los flujos que conforman el tráfico de uso de Internet dentro de una red permite tanto a empresas como a organismos una útil herramienta de gestión de los recursos de sus redes, así como la posibilidad de establecer políticas de prohibición o priorización de tráfico específico. La proliferación de nuevas aplicaciones y de nuevas técnicas han dificultado el uso de valores conocidos (well-known) en puertos de aplicaciones proporcionados por la IANA (Internet Assigned Numbers Authority) para la detección de dichas aplicaciones. Las redes P2P (Peer to Peer), el uso de puertos no conocidos o aleatorios, y el enmascaramiento de tráfico de muchas aplicaciones en tráfico HTTP y HTTPS con el fin de atravesar firewalls y NATs (Network Address Translation), entre otros, crea la necesidad de nuevos métodos de detección de tráfico. El objetivo de este estudio es desarrollar una serie de prácticas que permitan realizar dicha tarea a través de técnicas que están más allá de la observación de puertos y otros valores conocidos. Existen una serie de metodologías como Deep Packet Inspection (DPI) que se basa en la búsqueda de firmas, signatures, en base a patrones creados por el contenido de los paquetes, incluido el payload, que caracterizan cada aplicación. Otras basadas en el aprendizaje automático de parámetros de los flujos, Machine Learning, que permite determinar mediante análisis estadísticos a qué aplicación pueden pertenecer dichos flujos y, por último, técnicas de carácter más heurístico basadas en la intuición o el conocimiento propio sobre tráfico de red. En concreto, se propone el uso de alguna de las técnicas anteriormente comentadas en conjunto con técnicas de minería de datos como son el Análisis de Componentes Principales (PCA por sus siglas en inglés) y Clustering de estadísticos extraídos de los flujos procedentes de ficheros de tráfico de red. Esto implicará la configuración de diversos parámetros que precisarán de un proceso iterativo de prueba y error que permita dar con una clasificación del tráfico fiable. El resultado ideal sería aquel en el que se pudiera identificar cada aplicación presente en el tráfico en un clúster distinto, o en clusters que agrupen grupos de aplicaciones de similar naturaleza. Para ello, se crearán capturas de tráfico dentro de un entorno controlado e identificando cada tráfico con su aplicación correspondiente, a continuación se extraerán los flujos de dichas capturas. Tras esto, parámetros determinados de los paquetes pertenecientes a dichos flujos serán obtenidos, como por ejemplo la fecha y hora de llagada o la longitud en octetos del paquete IP. Estos parámetros serán cargados en una base de datos MySQL y serán usados para obtener estadísticos que ayuden, en un siguiente paso, a realizar una clasificación de los flujos mediante minería de datos. Concretamente, se usarán las técnicas de PCA y clustering haciendo uso del software RapidMiner. Por último, los resultados obtenidos serán plasmados en una matriz de confusión que nos permitirá que sean valorados correctamente. ABSTRACT. Being able to classify the applications that generate the traffic flows in an Internet network allows companies and organisms to implement efficient resource management policies such as prohibition of specific applications or prioritization of certain application traffic, looking for an optimization of the available bandwidth. The proliferation of new applications and new technics in the last years has made it more difficult to use well-known values assigned by the IANA (Internet Assigned Numbers Authority), like UDP and TCP ports, to identify the traffic. Also, P2P networks and data encapsulation over HTTP and HTTPS traffic has increased the necessity to improve these traffic analysis technics. The aim of this project is to develop a number of techniques that make us able to classify the traffic with more than the simple observation of the well-known ports. There are some proposals that have been created to cover this necessity; Deep Packet Inspection (DPI) tries to find signatures in the packets reading the information contained in them, the payload, looking for patterns that can be used to characterize the applications to which that traffic belongs; Machine Learning procedures work with statistical analysis of the flows, trying to generate an automatic process that learns from those statistical parameters and calculate the likelihood of a flow pertaining to a certain application; Heuristic Techniques, finally, are based in the intuition or the knowledge of the researcher himself about the traffic being analyzed that can help him to characterize the traffic. Specifically, the use of some of the techniques previously mentioned in combination with data mining technics such as Principal Component Analysis (PCA) and Clustering (grouping) of the flows extracted from network traffic captures are proposed. An iterative process based in success and failure will be needed to configure these data mining techniques looking for a reliable traffic classification. The perfect result would be the one in which the traffic flows of each application is grouped correctly in each cluster or in clusters that contain group of applications of similar nature. To do this, network traffic captures will be created in a controlled environment in which every capture is classified and known to pertain to a specific application. Then, for each capture, all the flows will be extracted. These flows will be used to extract from them information such as date and arrival time or the IP length of the packets inside them. This information will be then loaded to a MySQL database where all the packets defining a flow will be classified and also, each flow will be assigned to its specific application. All the information obtained from the packets will be used to generate statistical parameters in order to describe each flow in the best possible way. After that, data mining techniques previously mentioned (PCA and Clustering) will be used on these parameters making use of the software RapidMiner. Finally, the results obtained from the data mining will be compared with the real classification of the flows that can be obtained from the database. A Confusion Matrix will be used for the comparison, letting us measure the veracity of the developed classification process.

Sistema de ayuda a la selección de soluciones de protección de datos personales, para los productos y servicios en “Internet de las Cosas”

Los nuevos productos y servicios de “Internet de las Cosas” nos harán más eficientes, con una mayor capacidad de actuación y una mejor comprensión de nuestro entorno. Se desarrollarán nuevas ayudas técnicas que permitirán prolongar nuestra vida activa, y muchas ventajas que hoy día nos costaría imaginar. Sin embargo coexistiremos con una gran cantidad de dispositivos que recopilarán información sobre nuestra actividad, costumbres, preferencias, etc., que podrían amenazar nuestra privacidad. La desconfianza que estos riesgos podrían generar en las personas, actuaría como una barrera que podría dificultar el pleno desarrollo de esta nueva gama de productos y servicios. Internet de las Cosas, alcanza su significado más representativo con las Ciudades Inteligentes (Smart Cities) que proporcionan las herramientas necesarias para mejorar la gestión de las ciudades modernas de una manera mucho más eficiente. Estas herramientas necesitan recolectar información de los ciudadanos abriendo la posibilidad de someterlos a un seguimiento. Así, las políticas de seguridad y privacidad deben desarrollarse para satisfacer y gestionar la heterogeneidad legislativa en torno a los servicios prestados y cumplir con las leyes del país en el que se proporcionan estos servicios. El objetivo de esta tesis es aportar una posible solución para la garantizar la seguridad y privacidad de los datos personales en Internet de las Cosas, mediante técnicas que resulten de la colaboración entre las áreas empresarial, legislativa y tecnológica para dar confianza a todos los actores involucrados y resolver la posible colisión de intereses entre ellos, y también debe ser capaz de poder gestionar la heterogeneidad legislativa. Considerando que gran parte de estos servicios se canalizan a través de redes de sensores inalámbricos, y que estas redes tienen importantes limitaciones de recursos, se propone un sistema de gestión que además sea capaz de dar una cobertura de seguridad y privacidad justo a medida de las necesidades. ABSTRACT New products and services offered by the “Internet of Things” will make people more efficient and more able to understand the environment and take better decisions. New assistive technologies will allow people to extend their working years and many other advantages that currently are hard to foreseen. Nonetheless, we will coexist with a large number of devices collecting information about activities, habits, preferences, etc. This situation could threaten personal privacy. Distrust could be a barrier to the full development of these new products and services. Internet of Things reaches its most representative meaning by the Smart Cities providing the necessary solutions to improve the management of modern cities by means of more efficient tools. These tools require gathering citizens’ information about their activity, preferences, habits, etc. opening up the possibility of tracking them. Thus, privacy and security policies must be developed in order to satisfy and manage the legislative heterogeneity surrounding the services provided and comply with the laws of the country where they are provided. The objective of this thesis is to provide a feasible solution to ensure the security and privacy of personal data on the Internet of Things through resulting techniques from the collaboration between business, legislative and technological areas so as to give confidence to all stakeholders and resolve the possible conflict of interest between them, as well as to manage the legislative heterogeneity. Whereas most of these services are based on wireless sensor networks, and these networks have significant resource constraints, the proposed management system is also able to cover the security and privacy requirements considering those constrains.

Adaptation strategies for high order discontinuous Galerkin methods based on Tau-estimation

In this paper three p-adaptation strategies based on the minimization of the truncation error are presented for high order discontinuous Galerkin methods. The truncation error is approximated by means of a ? -estimation procedure and enables the identification of mesh regions that require adaptation. Three adaptation strategies are developed and termed a posteriori, quasi-a priori and quasi-a priori corrected. All strategies require fine solutions, which are obtained by enriching the polynomial order, but while the former needs time converged solutions, the last two rely on non-converged solutions, which lead to faster computations. In addition, the high order method permits the spatial decoupling for the estimated errors and enables anisotropic p-adaptation. These strategies are verified and compared in terms of accuracy and computational cost for the Euler and the compressible Navier?Stokes equations. It is shown that the two quasi- a priori methods achieve a significant reduction in computational cost when compared to a uniform polynomial enrichment. Namely, for a viscous boundary layer flow, we obtain a speedup of 6.6 and 7.6 for the quasi-a priori and quasi-a priori corrected approaches, respectively.