La conception originale d’Internet n’a pas pris en compte les aspects de sécurité du réseau; l’objectif prioritaire était de faciliter le processus de communication. Par conséquent, de nombreux protocoles de l’infrastructure Internet exposent un ensemble de vulnérabilités. Ces dernières peuvent être exploitées par les attaquants afin de mener un ensemble d’attaques. Les attaques par déni de service distribué (Distributed Denial of Service ou DDoS) représentent une grande menace et l’une des attaques les plus dévastatrices causant des dommages collatéraux aux opérateurs de réseau ainsi qu’aux fournisseurs de services Internet. Les réseaux programmables, dits Software-Defined Networking (SDN), ont émergé comme un nouveau paradigme promettant de résoudre les limitations de l’architecture réseau actuelle en découplant le plan de contrôle du plan de données. D’une part, cette séparation permet un meilleur contrôle du réseau et apporte de nouvelles capacités pour mitiger les attaques par déni de service distribué. D’autre part, cette séparation introduit de nouveaux défis en matière de sécurité du plan de contrôle. L’enjeu de cette thèse est double. D’une part, étudier et explorer l’apport de SDN à la sécurité afin de concevoir des solutions efficaces qui vont mitiger plusieurs vecteurs d’attaques. D’autre part, protéger SDN contre ces attaques. À travers ce travail de recherche, nous contribuons à la mitigation des attaques par déni de service distribué sur deux niveaux (intra-domaine et inter-domaine), et nous contribuons au renforcement de l’aspect sécurité dans les réseaux programmables. / The original design of Internet did not take into consideration security aspects of the network; the priority was to facilitate the process of communication. Therefore, many of the protocols that are part of the Internet infrastructure expose a set of vulnerabilities that can be exploited by attackers to carry out a set of attacks. Distributed Denial-of-Service (DDoS) represents a big threat and one of the most devastating and destructive attacks plaguing network operators and Internet service providers (ISPs) in a stealthy way. Software defined networks (SDN), an emerging technology, promise to solve the limitations of the conventional network architecture by decoupling the control plane from the data plane. On one hand, the separation of the control plane from the data plane allows for more control over the network and brings new capabilities to deal with DDoS attacks. On the other hand, this separation introduces new challenges regarding the security of the control plane. This thesis aims to deal with various types of attacks including DDoS attacks while protecting the resources of the control plane. In this thesis, we contribute to the mitigation of both intra-domain and inter-domain DDoS attacks, and to the reinforcement of security aspects in SDN.

[pt] Com o rápido crescimento da Internet das Coisas (IoT), surgiram novas tecnologias de comunicação sem fio para atender aos requisitos de longo alcance, baixo custo e baixo consumo de energia exigidos pelos aplicativos de IoT. Nesse contexto, surgiram as redes de longa distância de baixa potência (LPWANs), as quais oferecem diferentes soluções que atendem aos requisitos dos aplicativos de IoT mencionados anteriormente. Entre as soluções LPWAN existentes, o LoRaWAN tem-se destacado por receber atenção significativa da indústria e da academia nos últimos anos. Embora o LoRaWAN ofereça uma combinação atraente de transmissões de dados de longo alcance e baixo consumo de energia, ele ainda enfrenta vários desafios em termos de confiabilidade e escalabilidade. No entanto, devido a sua natureza de código aberto e à flexibilidade do esquema de modulação no qual ele se baseia (Long Range (LoRa) permite o ajuste de fatores de espalhamento e a potência de transmissão), o LoRaWAN também oferece importantes possibilidades de melhorias. Esta dissertação aproveita a adequação dos algoritmos de Aprendizagem por Reforço (RL) para resolver tarefas de tomada de decisão e os utiliza para ajustar dinamicamente os parâmetros de transmissão dos dispositivos finais LoRaWAN. O sistema proposto, chamado RL-LoRa, mostra melhorias significativas em termos de confiabilidade e escalabilidade quando comparado ao LoRaWAN. Especificamente, diminui a taxa de erro de pacote (PER) média do LoRaWAN em 15 porcento, o que pode aumentar ainda mais a escalabilidade da rede. / [en] With the massive growth of the Internet of Things (IoT), novel wireless communication technologies have emerged to address the long-range, lowcost, and low-power consumption requirements of the IoT applications. In this context, the Low Power Wide Area Networks (LPWANs) have appeared, offering different solutions that meet the IoT applications requirements mentioned before. Among the existing LPWAN solutions, LoRaWAN has stood out for receiving significant attention from both industry and academia in recent years. Although LoRaWAN offers a compelling combination of long-range and low-power consumption data transmissions, it still faces several challenges in terms of reliability and scalability. However, due to its open-source nature and the flexibility of the modulation scheme it is based on (Long Range (LoRa) modulation allows the adjustment of spreading factors and transmit power), LoRaWAN also offers important possibilities for improvements. This thesis takes advantage of the appropriateness of the Reinforcement Learning (RL) algorithms for solving decision-making tasks, and use them to dynamically adjust the transmission parameters of LoRaWAN end devices. The proposed system, called RL-LoRa, shows significant improvements in terms of reliability and scalability when compared with LoRaWAN. Specifically, it decreases the average Packet Error Ratio (PER) of LoRaWAN by 15 percent, which can further increase the network scalability.

Partial discharge (PD) measurements have been widely accepted as an efficient online insulation condition assessment method in high voltage equipment. Two sets of experimental PD measuring setups were established with the aim to study the variations in the partial discharge characteristics over the insulation degradation in terms of the physical phenomena taking place in PD sources, up to the point of failure. Probabilistic lifetime modeling techniques based on classification, regression and multivariate time series analysis were performed for a system of PD response variables, i.e. average charge, pulse repetition rate, average charge current, and largest repetitive discharge magnitude over the data acquisition period. Experimental lifelong PD data obtained from samples subjected to accelerated degradation was used to study the dynamic trends and relationships among those aforementioned response variables. Distinguishable data clusters detected by the T-Stochastics Neighborhood Embedding (tSNE) algorithm allows for the examination of the state-of-the-art modeling techniques over PD data. The response behavior of trained models allows for distinguishing the different stages of the insulation degradation. An alternative approach utilizing a multivariate time series analysis was performed in parallel with Classification and Regression models for the purpose of forecasting PD activity (PD response variables corresponding to insulation degradation). True observed data and forecasted data mean values lie within the 95th percentile confidence interval responses for a definite horizon period, which demonstrates the soundness and accuracy of models. A life-predicting model based on the cointegrated relations between the multiple response variables, trained model responses correlated with experimentally evaluated time-to-breakdown values and well-known physical discharge mechanisms, can be used to set an emergent alarming trigger and as a step towards establishing long-term continuous monitoring of partial discharge activity. Furthermore, this dissertation also proposes an effective PD monitoring system based on wavelet and deflation compression techniques required for an optimal data acquisition as well as an algorithm for high-scale, big data reduction to minimize PD data size and account only for the useful PD information. This historically recorded useful information can thus be used for, not only postault diagnostics, but also for the purpose of improving the performance of modelling algorithms as well as for an accurate threshold detection.

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