HTTP botnets are currently the most popular form of botnets compared to IRC and P2P botnets. This is because, they are not only easier to implement, operate, and maintain, but they can easily evade detection. Likewise, HTTP botnets flows can easily be buried in the huge volume of legitimate HTTP traffic occurring in many organizations, which makes the detection harder. In this thesis, a new detection framework involving three detection models is proposed, which can run independently or in tandem. The first detector profiles the individual applications based on their interactions, and isolates accordingly the malicious ones. The second detector tracks the regularity in the timing of the bot DNS queries, and uses this as basis for detection. The third detector analyzes the characteristics of the domain names involved in the DNS, and identifies the algorithmically generated and fast flux domains, which are staples of typical HTTP botnets. Several machine learning classifiers are investigated for each of the detectors. Experimental evaluation using public datasets and datasets collected in our testbed yield very encouraging performance results. / Graduate

in English Abstract The thesis aims to fathom different aspects of the commodification of personal data. The topic is an issue of current interest. On one hand, due to the constant improvement of the technology of collecting and processing personal data, individuals are becoming very vulnerable. Techniques of influencing their thinking and decision-making based on their personal data have become so subtle and insidious that they often go unnoticed by the individuals. On the other hand, the development of the data economy can contribute to many goals that benefit society. This paper aims to answer two research questions. The first research question is how to guarantee the secure processing of personal data and to eliminate negative impacts on the subject and on society. The second research question concerns both ensuring adequate access of digital industry to personal data and empowering the data subject. Given the complexity of the issue and the confusion about the meaning and interpretation of basic concepts such as personal data and information, it is necessary to first briefly outline the nature and characteristics of personal data. The next two chapters analyse the legal means to achieve the objectives set out in the research questions. The final chapter provides a summary of recommendations on...

This thesis presents an artificial neural network (ANN) approach to diagnose and detect faults in oil-filled power transformers based on dissolved gas-in-oil analysis. The goal of the research is to investigate the available transformer incipient fault diagnosis methods and then develop an ANN approach for this purpose. This ANN classifier should not only be able to detect the fault type, but also should be able to judge the cellulosic material breakdown. This classifier should also be able to accommodate more than one type of fault. This thesis describes a two-step ANN method that is used to detect faults with or without cellulose involved. Utilizing a feedforward artificial neural network, the classifier was trained with back-propagation, using training samples collected from different failed transformers. It is shown in the thesis that such a neural-net based approach can yield a high diagnosis accuracy. Several possible design alternatives and comparisons are also addressed in the thesis. The final system has been successfully tested, exhibiting a classification accuracy of 95% for major fault type and 90% for cellulose breakdown. / Master of Science

High voltage power transformer is one of the most important and expensive components in today's power transmission and distribution systems. Any overlooked critical fault generated inside a power transformer may lead to a transformer catastrophic failure which could not only cause a disruption to the power system but also significant equipment damage. Accurate and prompt information on the health state of a transformer is thus the critical prerequisite for an asset manager to make a vital decision on a transformer with suspicious conditions. Partial discharge (PD) is not only a precursor of insulation degradation, but also a primary factor to accelerate the deterioration of the insulation system in a transformer. Monitoring of PD activities and the concentration of PD generated combustible gases dissolved in the transformer oil has been proven to be an effective procedure for transformer health state estimation. However current commercially available sensors can only be installed outside of transformers and offer indirect or delayed information. This research is aimed to investigate and develop several sensor techniques for transformer health monitoring. The first work is an optical fiber extrinsic Fabry-Perot interferometric sensor for PD detection. By filling SF6 into the sensor air cavity of the extrinsic Fabry-Perot interferometer sensor, the last potential obstacle that prevents this kind of sensors from being installed inside transformers has been removed. The proposed acoustic sensor multiplexing system is stable and more economical than the other sensor multiplexing methods that usually require the use of a tunable laser or filters. Two dissolved gas analysis (DGA) methods for dissolved hydrogen or acetylene measurement are also proposed and demonstrated. The dissolved hydrogen detection is based on hydrogen induced fiber loss and the dissolved acetylene detection is by direct oil transmission measurement. / Ph. D.

Esta pesquisa apresenta uma metodologia, baseada em duas técnicas não invasivas para identificação e diagnóstico de pontos quentes em transformadores de potência durante plena operação ou em fase de projeto. A primeira técnica é baseada na medição de radiação infravermelha, emitida pelo equipamento em funcionamento e registrada por meio de análise termográfica. A segunda técnica é possível a partir do conhecimento prévio das características construtivas do transformador, fazendo uso do Método dos Elementos Finitos (MEF). A segunda técnica pode ser validada a partir das medições realizadas utilizando a primeira técnica. A formação de gases no interior dos transformadores de potência, devido à elevação da temperatura do óleo isolante em função dos pontos quentes, é discutida em detalhes com base nas normas técnicas estabelecidas pelo IEEE e IEC. As técnicas e procedimentos abordados ao longo dessa pesquisa foram obtidos a partir de um transformador com potência nominal de 120 MVA e relação de tensão 13,8/230 kV, projetado para integrar uma fazenda eólica ao Sistema Interligado Nacional (SIN). / This research presents a methodology based on two noninvasive techniques for identification and diagnostic of hot spots in power transformers during operation or project development. The first is based on measurements of infrared radiation from the equipment during operation and recording by thermography. The second technique is possible from the previous knowing of the constructive characteristics of the power transformer, by using the Finite Element Method (FEM). The second technique can be validated from measurements obtained using the first technique. The gas formation into the power transformers, because of the high temperatures in the insulating oil due to the hot spots, is discussed in details based on normative recommendations well established by the IEEE and IEC. All techniques and procedures to be approached in this research were obtained using a 120-MVA power transformer with voltage relationship of 13.8/230 kV that was projected to interconnect a wind farm to the Interconnected Brazilian System (Sistema Interligado Nacional - SIN).

Esta pesquisa apresenta uma metodologia, baseada em duas técnicas não invasivas para identificação e diagnóstico de pontos quentes em transformadores de potência durante plena operação ou em fase de projeto. A primeira técnica é baseada na medição de radiação infravermelha, emitida pelo equipamento em funcionamento e registrada por meio de análise termográfica. A segunda técnica é possível a partir do conhecimento prévio das características construtivas do transformador, fazendo uso do Método dos Elementos Finitos (MEF). A segunda técnica pode ser validada a partir das medições realizadas utilizando a primeira técnica. A formação de gases no interior dos transformadores de potência, devido à elevação da temperatura do óleo isolante em função dos pontos quentes, é discutida em detalhes com base nas normas técnicas estabelecidas pelo IEEE e IEC. As técnicas e procedimentos abordados ao longo dessa pesquisa foram obtidos a partir de um transformador com potência nominal de 120 MVA e relação de tensão 13,8/230 kV, projetado para integrar uma fazenda eólica ao Sistema Interligado Nacional (SIN). / This research presents a methodology based on two noninvasive techniques for identification and diagnostic of hot spots in power transformers during operation or project development. The first is based on measurements of infrared radiation from the equipment during operation and recording by thermography. The second technique is possible from the previous knowing of the constructive characteristics of the power transformer, by using the Finite Element Method (FEM). The second technique can be validated from measurements obtained using the first technique. The gas formation into the power transformers, because of the high temperatures in the insulating oil due to the hot spots, is discussed in details based on normative recommendations well established by the IEEE and IEC. All techniques and procedures to be approached in this research were obtained using a 120-MVA power transformer with voltage relationship of 13.8/230 kV that was projected to interconnect a wind farm to the Interconnected Brazilian System (Sistema Interligado Nacional - SIN).

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / O monitoramento e o diagnÃstico de falhas incipientes em transformadores de potÃncia imersos em Ãleo estÃo diretamente relacionados à avaliaÃÃo das condiÃÃes do sistema de isolamento. Neste estudo, estabelece-se o conceito de monitoramento e diagnÃstico, e em seguida tÃcnicas de monitoramento on-line sÃo discutidas. Um sistema de prÃ-diagnÃstico à elaborado baseado na utilizaÃÃo de um dispositivo on-line de monitoramento, Hydran da GE, para classificar a gravidade da situaÃÃo de falha detectada. Uma vez detectada uma situaÃÃo de falha, mÃdulos inteligentes de diagnÃstico de falhas incipientes, via redes neurais, podem ser utilizados para identificaÃÃo da falha interna do equipamento. Para completar a verificaÃÃo da qualidade dielÃtrica do lÃquido isolante, tambÃm à descrito um algoritmo inteligente, baseado em redes neurais, para diagnÃstico do estado do Ãleo atravÃs das grandezas fÃsico-quÃmicas. A relaÃÃo entre os atributos fÃsico-quÃmicos e as grandezas cromatogrÃficas referente ao Ãleo mineral tambÃm foram averiguadas. Foi desenvolvida, entÃo, a estimaÃÃo dos gases dissolvidos atravÃs das caracterÃsticas fÃsico-quÃmicas. Os mÃdulos de monitoramento on-line, diagnÃsticos do estado do Ãleo e de falhas incipientes, alÃm da estimaÃÃo dos gases dissolvidos, perfazem um sistema computacional de auxÃlio à operaÃÃo e manutenÃÃo. O sistema implementado apresenta resultados satisfatÃrios na implantaÃÃo em uma planta de usina termelÃtrica. / The monitoring and diagnosis of incipient fault in power transformers immerses in oil are directly related to the assessment of the isolation system conditions. In this research, it is established the concept of monitoring and diagnosis, after that, on-line monitoring techniques are discussed. A pre-diagnosis system is elaborated based on use of a monitoring on-line device, Hydran GE, to classify the situation gravity of the detected fault. Once detected a fault situation, intelligent modules of incipient fault diagnosis, by neural networks, can be used to identification of internal fault of the equipment. To complete the checking of the dielectric quality of the isolate liquid, it is also described an intelligent algorithm, based on neural networks, to diagnosis of the oil estate through physical-chemical attribute. The relation between physical-chemical attributes and chromatographic ones regarding to mineral oil were also verified. It was developed, then, the dissolved gases esteem through physicalchemical characteristics. The on-line monitoring modules, diagnosis of oil estate and incipient fault, besides dissolved gases esteem, constitute a computation aid system to operation and maintenance. The implemented system presents satisfied results in a thermoelectric power plant.

This dissertation is a systematic study of artificial intelligence (AI) applications for the diagnosis of power transformer incipient fault. The AI techniques include artificial neural networks (ANN, or briefly neural networks - NN), expert systems, fuzzy systems and multivariate regression. The fault diagnosis is based on dissolved gas-in-oil analysis (DGA). A literature review showed that the conventional fault diagnosis methods, i.e. the ratio methods (Rogers, Dornenburg and IEC) and the key gas method, have limitations such as the "no decision" problem. Various AI techniques may help solve the problems and present a better solution. Based on the IEC 599 standard and industrial experiences, a knowledge-based inference engine for fault detection was developed. Using historical transformer failure data from an industrial partner, a multi-layer perceptron (MLP) modular neural network was identified as the best choice among several neural network architectures. Subsequently, the concept of a hybrid diagnosis was proposed and implemented, resulting in a combined neural network and expert system tool (the ANNEPS system) for power transformer incipient diagnosis. The abnormal condition screening process, as well as the principle and algorithms of combining the outputs of knowledge based and neural network based diagnosis, were proposed and implemented in the ANNEPS. Methods of fuzzy logic based transformer oil/paper insulation condition assessment, and estimation of oil sampling interval and maintenance recommendations, were also proposed and implemented. Several methods of power transformer incipient fault location were investigated, and a 7Ã 21Ã 5 MLP network was identified as the best choice. Several methods for on-load tap changer (OLTC) coking diagnosis were also investigated, and a MLP based modular network was identified as the best choice. Logistic regression analysis was identified as a good auditor in neural network input pattern selection processes. The above results can help developing better power transformer maintenance strategies, and serve as the basis of on-line DGA transformer monitors. / Ph. D.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / O diagnÃstico de falhas incipientes em transformadores de potÃncia imersos em Ãleo està diretamente relacionado à avaliaÃÃo das condiÃÃes do sistema de isolamento. Este estudo aborda a relaÃÃo entre os gases dissolvidos no Ãleo e a qualidade do Ãleo mineral isolante utilizado em transformadores de potÃncia. As redes neurais artificiais sÃo utilizadas na abordagem da avaliaÃÃo das condiÃÃes operacionais do Ãleo isolante em transformadores de potÃncia, que à caracterizada por um comportamento dinÃmico nÃo-linear. As condiÃÃes de operaÃÃo e a integridade do sistema de isolamento de um transformador de potÃncia podem ser inferidas atravÃs das anÃlises fÃsico-quÃmicas e cromatogrÃficas (AnÃlise de GÃs Dissolvido). Estes ensaios permitem estabelecer procedimentos de operaÃÃo e manutenÃÃo do equipamento e normalmente sÃo realizados simultaneamente. Esta tese de doutorado propÃe um mÃtodo que pode ser usado para extrair informaÃÃes cromatogrÃficas usando as anÃlises fÃsico-quÃmicas atravÃs de redes neurais artificiais. As anÃlises atuais das propriedades fÃsico-quÃmicas fornecem apenas diagnÃstico do estado do Ãleo, o que nÃo permite o diagnÃstico de falhas incipientes. Acredita-se que, as concessionÃrias de energia podem melhorar a confiabilidade na previsÃo de falhas incipientes a um custo menor com este mÃtodo, uma vez que apenas um ensaio à necessÃrio. Os resultados mostraram que esta estratÃgia à promissora com mÃdia de acertos em diagnÃsticos de falhas maiores que 72%. O objetivo deste trabalho à a aplicaÃÃo direta do diagnÃstico de falhas incipientes atravÃs da utilizaÃÃo de propriedades fÃsico-quÃmicas, sem a necessidade de fazer uma cromatografia do Ãleo. / The diagnosis of incipient fault in power transformers immerses in oil are directly related to the assessment of the isolation system conditions. This search is about the relationship between dissolved gases and the quality of the insulating mineral oil used in power transformers. Artificial Neural Networks are used to approach operational conditions assessment issue of the insulating oil in power transformers, which is characterized by a nonlinear dynamic behavior. The operation conditions and integrity of a power transformer can be inferred by analysis of physicochemical and chromatographic (DGA â Dissolved Gas Analysis) profiles of the isolating oil. This tests allow establishing procedures for operating and maintaining the equipment and usually are performed simultaneously. This work proposes a method that can be used to extract chromatographic information using physicochemical analysis through Artificial Neural Networks. The present analysis of physicochemical properties only provide a diagnostic tool for the oil quality, which does not allow the diagnosis of incipient faults. ItÂs believed that, the power utilities could improve reliability in the prediction of incipient failures at a lower cost with this method, since only one test is required. The results show this strategy might be promising with an average accuracy for diagnosis of faults greater than 72%. The purpose of this work is the direct implementation of the diagnosis of incipient faults through the use of physicochemical properties without the need to make an oil chromatography.

Les répétitions en tandem sont constituées de successions de motifs d'ADN. Ces structures sont présentes dans tous les organismes, procaryotes comme eucaryotes et, même si leur rôle biologique est encore peu compris, elles ont des applications dans de nombreux domaines. Tout d'abord, chez les bactéries, les répétitions en tandem polymorphes, dont le nombre d'unités varie, se révèlent un outil puissant pour l'identification de souches à des fins épidémiologiques. Par ailleurs, certaines répétitions en tandem humaines ont la propriété de muter à des fréquences élevées : les minisatellites hypermutables sont les éléments les plus instables du génome humain. Ils peuvent être utilisés comme biomarqueurs d'exposition à des agents potentiellement mutagènes tels que les radiations ionisantes. D'un point de vue plus fondamental, ils sont également un modèle d'étude des mécanismes d'instabilité des génomes. Dans cette thèse, nous mettons à profit les données issues du séquençage afin d'identifier des répétitions en tandem polymorphes. Nous avons tout d'abord élaboré une base de données des répétitions en tandem accessible sur le web (http://minisatellites.u-psud.fr), qui fournit un accès aux répétitions en tandem de génomes entiers. Ensuite, dans le but de sélectionner les répétitions en tandem polymorphes, plusieurs stratégies ont été mises en oeuvre. D'une part, chez les bactéries pour lesquelles les séquences de plusieurs souches étaient disponibles, nous avons créé un utilitaire de comparaison de souches, afin d'identifier des marqueurs polymorphes utilisables en épidémiologie. D'autre part, une étude menée sur les minisatellites humains a permis de définir des critères prédictifs du polymorphisme à partir de la séquence d'un seul allèle de minisatellite, et a en outre mis en évidence un nouveau minisatellite hypermutable situé dans une séquence codante putative. Les critères prédictifs ont également été appliqués à l'identification de minisatellites codants potentiellement polymorphes dans le génome humain.