Mestrado em Ciência da Computação (INF)

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http://200.137.215.59/tede/handle/tde/225

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Detecção online de dispositivos sem fio intrusos usando o sinal eletromagnético de transmissão
(Universidade Federal de Goiás, 2022-09-23) Abreu, Marcos Felipe Barboza de; Vieira, Flávio Henrique Teles; http://lattes.cnpq.br/0920629723928382; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Klautau Júnior, Aldebaro Barreto da Rocha; Corrêa, Sand Luz; Cardoso, Kleber Vieira; Vieira, Flávio Henrique Teles
The identification of Internet of Things (IoT) devices through the electromagnetic signal is a topic widely investigated in the literature, and this technique is considered highly accurate by several works. The use of offline techniques, that is, when there is no presence of new devices, is widely explored, but so far, systems are not found effectively using the detection of unknown devices in the online way, i.e. , one of the greatest potentials of this type of technique has not been investigated. This work presents an online system that differentiates authentic devices from intrusive devices. For this, the use of the probability matrix of classifiers is explored, aiming to identify unknown devices by them. In addition to the technique, it is also presented a system that features a modular, extensible and generic architecture, which aims to minimally interfere with the normal flow of an Internet of Things application. The system is implemented using the GNU Radio tool and experiments are presented, which aim to show the feasibility of the technique. The entire discussion is based on data collected from real environments, using devices from wireless communication technologies LoRa and ZigBee. In addition, the work analyzed data from WiFi technology, from collections found in the literature. Tests show that it is possible to identify unknown devices in the order of milliseconds, with a low error rate.
Alocação de recursos e posicionamento de funções virtualizadas em redes de acesso por rádio desagregadas
(Universidade Federal de Goiás, 2023-08-30) Almeida, Gabriel Matheus Faria de; Pinto, Leizer de Lima; http://lattes.cnpq.br/0611031507120144; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Cardoso, Kleber Vieira; Pinto, Leizer de Lima; Klautau Júnior, Aldebaro Barreto da Rocha; Silva, Luiz Antonio Pereira da
Jointly choosing a functional split of the protocol stack and placement of network functions in a virtualized RAN is critical to efficiently using the access network resources. This problem represents a current research topic in 5G and Post-5G networks, which involves the challenge of simultaneously choosing the placement of virtualized functions, the routes for traffic and the management of available computing resources. In this work, we present three approaches to solve this problem considering the planning scenario and two approaches considering the network operation scenario. The first result is a Mixed Integer Linear Programming (MILP) model, considering a generic set of processing nodes and multipath routing. The second approach uses artificial intelligence and machine learning concepts, in which we formulate a deep reinforcement learning agent. The third approach used is based on search meta-heuristics, through a genetic algorithm. The last two approaches are Markov Decision Process (MDP) formulations that consider dynamic demand on radio units. In all formulations, the objective is to maximize the network function’s centralization while minimizing positioning cost. Analysis of the solutions and comparison of their results show that exact approaches such as MILP naturally provide the best solution. However, in terms of efficiency, the genetic algorithm has the best search time, finding a high quality solution in a few seconds. The deep reinforcement learning agent presents a high convergence, finding high quality solutions for the problem and showing problem generalization capacity with different topologies. Finally, the formulations considering the network operation scenario with dynamic demand are highly complex due to the size of the action space
Algoritmos baseados em estratégia evolutiva para a seleção dinâmica de espectro em rádios cognitivos
(Universidade Federal de Goiás, 2013-11-22) Barbosa, Camila Soares; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Cardoso, Kleber Vieira; Corrêa, Sand Luz; Camilo Junior, Celso Gonçalves; Santos, Aldri Luiz dos
One of the main challenges in Dynamic Spectrum Selection for Cognitive Radios is the choice of the frequency range for each transmission. This choice should minimize interference with legacy devices and maximize the discovering opportunities or white spaces. There are several solutions to this issue, and Reinforcement Learning algorithms are the most successful. Among them stands out the Q-Learning whose weak point is the parameterization, since adjustments are needed in order to reach successfully the proposed objective. In that sense, this work proposes an algorithm based on evolutionary strategy and presents the main characteristics adaptability to the environment and fewer parameters. Through simulation, the performance of the Q-Learning and the proposal of this work were compared in different scenarios. The results allowed to evaluate the spectral efficiency and the adaptability to the environment. The proposal of this work shows promising results in most scenarios.
Caracterização e modelagem do comportamento de usuários de mapas Web para reprodução de carga de trabalho e avaliação de desempenho de sistemas baseados em tiles
(Universidade Federal de Goiás, 2015-11-10) Braga, Vinícius Gonçalves; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Cardoso, Kleber Vieira; Almeida, Jussara Marques de; Rocha, Ricardo Couto Antunes da; Rodrigues, Vagner José do Sacramento
Web mapping systems, or Web GIS, are important tools for geographic orientation and spatial data analysis. In recent years, the use of these systems has increased, as well as the challenge to ensure performance while the number of users and the data volume continue to grow. The performance evaluation is an important activity to investigate and mitigate systems performance issues. The workload is the starting point of the performance evaluation and is responsible for sending resquests to the system under evaluation. The reliability of the evaluation results is related to an appropriate load. Despite the importance of web map systems, the literature as presented little efforts to model the workload of these systems. In this dissertation, we present a methodology to collect and analyze data in order to create a model of Web GIS users’ behavior and to instantiate the model in a workload generator. We also propose a generic model, named MUSe-GM (Maps User Session Generative Model), and present a characterization of the users’ behavior using data of the access to a popular mapping application, collected by an extension developed for the Google Chrome browser. The characterization results were used to develop an instance of the behavior model and to implement a workload generator. The instance was evaluated by testing in a realWeb map system, using the workload generator, and through simulations. The results were compared with two other models from literature. The proposed model in this dissertation was significantly different in several aspects compared to the other, presenting a behavior closer to the real users’ behavior.
Estratégias para uso eficiente de recursos em centros de dados considerando consumo de CPU e RAM
(Universidade Federal de Goiás, 2014-08-04) Castro, Pedro Henrique Pires de; Corrêa, Sand Luz; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Cardoso, Kleber Vieira; Corrêa, Sand Luz; Costa, Fábio Moreira; Granville, Lisandro Zambenedetti
Cloud computing is being consolidated as a new distributed systems paradigm, offering computing resources in a virtualized way and with unprecedented levels of flexibility, reliability, and scalability. Unfortunately, the benefits of cloud computing come at a high cost with regard to energy, mainly because of one of its core enablers, the data center. There are a number of proposals that seek to enhance energy efficiency in data centers. However, most of them focus only on the energy consumed by CPU and ignore the remaining hardware, e.g., RAM. In this work, we show the considerable impact that RAM can have on total energy consumption, particularly in servers with large amounts of this memory. We also propose three new approaches for dynamic consolidation of virtual machines (VMs) that take into account both CPU and RAM usage. We have implemented and evaluated our proposals in the CloudSim simulator using real-world traces and compared the results with state-of-the-art solutions. By adopting a wider view of the system, our proposals are able to reduce not only energy consumption but also the number of SLA violations, i.e., they provide a better service at a lower cost.
Detecção online de agregações hierárquicas bidimensionais de fluxos em redes definidas por software
(Universidade Federal de Goiás, 2014-12-16) Cruz, Mário Augusto da; Corrêa, Sand Luz; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Cardoso, Kleber Vieira; Corrêa, Sand Luz; Rosa, Thierson Couto; Abelém, Antônio Jorge Gomes
Software Defined Networking represents a new paradigm that eases the operation, monitoring and network managing through the decoupling between the control plane and the data plane. However, in this new context, some classic solutions in the network monitoring field need to be revisited, as there are new constraints, but there are also new opportunities. In monitoring context, one strategy commonly used, mainly in high capacity networks, is the tracking of the most frequent items, also known as heavy hitters. One approach to monitoring the most frequent items consists in detecting the hierarchical heavy hitters, which allows an efficient real time monitoring. In this work, we propose and evaluate a new monitoring solution capable of online detection of hierarchical heavy hitters, using the characteristics of software defined networks, in special the OpenFlow protocol. Our proposal, combines a flexible accounting of flow rules, from OpenFlow switches, with inspection of traffic samples through a dedicated device. We evaluate our proposal in a simulated and emulated environments, both using packet traces generated artificially and also from real networks. The results show that our proposal has satisfactory accuracy and low convergence time in comparison to a previous solution to OpenFlow networks, in addition to identify heavy hitters in two dimensions.
Classificação dinâmica de nós em redes em malha sem fio
(2014-09-11) Guedes, Diego Américo; Ziviani, Artur; http://lattes.cnpq.br/0472856771871140; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424
In this work we present and evaluate a modeling methodology that describes the creation of a topology for wireless mesh networks, and how this topology changes over time. The modeling methodology is based on network science, which is a multidisciplinary research area that has a lot of tools to help in the study and analysis of networks. In wireless mesh networks, the relative importance of the nodes is often related to the topological aspects, and data flow. However, due to the dynamics of the network, the relative importance of the nodes may vary in time. In the context of network science, the concept of centrality metric represents the relative importance of a node in the network. In this work we show also that the current centrality metrics are not able to rank properly the nodes in wireless mesh networks. Then we propose a new metric of centrality that ranks the most important nodes in a wireless mesh network over time. We evaluate our proposal using data from a case study of the proposed modeling methodology and also from real wireless mesh networks, achieving satisfactory performance. The characteristics of our metric make it a useful tool for monitoring dynamic networks.
Alocação de recursos para redes LTE (Long Term Evolution) em bandas não-licenciadas
(Universidade Federal de Goiás, 2018-09-28) Lima, Henrique Valle de; Bueno, Elivelton Ferreira; http://lattes.cnpq.br/2764240045623948; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Cardoso, Kleber Vieira; Bueno, Elivelton Ferreira; Abousheaisha, Abdallah S. Abdallah; Pinto, Leizer de Lima; Vieira, Flávio Henrique Teles
LTE (Long Term Evolution) in unlicensed band (LTE-U) has emerged as a promising solution to the problem of the huge growth in mobile data traffic. It expands the benefits of LTE with bands of the unlicensed 5 GHz spectrum, mainly used in IEEE 802.11. But uncertainties as to the availability of these bands make the adoption of LTE-U a great challenge. In this dissertation, we propose a mixed linear programming approach for allocating resources in order to expand service by LTE-U. Subsequently, we propose a stochastic programming approach, taking into account the randomness of the unlicensed band, allocating resources under a certain control in the risk of conflict with IEEE-802.11. We used three models of the literature for the demand for IEEE 802.11 in our tests. The results highlight the importance of prior knowledge about the distribution of this demand.
Operações espaciais robustas à imprecisão nas coordenadas geográficas
(Universidade Federal de Goiás, 2017-08-21) Oliveira, Welder Batista de; Rodrigues, Vagner José do Sacramento; http://buscacv.cnpq.br/buscacv/#/espelho? nro_id_cnpq_cp_s=4148896613580056; Cardoso, Kleber Vieira; http://buscacv.cnpq.br/buscacv/#/espelho? nro_id_cnpq_cp_s=0268732896111424; Cardoso, Kleber Vieira; Rodrigues, Vagner José do Sacramento; Davis Junior, Clodoveu Augusto; Santos, Helton Saulo Bezerra dos
Geographic Information Systems have revolutionized geographic research over the past three decades. These systems commonly provide a number of features for processing andanalyzing spatial data, such as spatial join and skyline. Although relevant, the effectiveness of such functionalities is affected by the imprecision of the geographic coordinates obtained by the georeferencing method employed. Moreover, the error contained in the coordinates may present several distributional patterns, which demands the development of solutions that are generalist concerning the error pattern that they can handle properly. Finally, spatial operations are already computationally expensive in their deterministic version, which is aggravated by the introduction of the stochastic component. The pre-sent work presents a general structure of spatial operations solutions robust to imprecise coordinates based on the use of simulations and probabilistic adaptations of heuristics in the literature. In addition, to deal with the problems mentioned, the proposed structure is designed to contemplate the requirements of generality, accuracy and efficiency at levels that enable its practical application. The overall solution structure is composed of the combination of probabilistic versions of heuristics of the deterministic versions of the spatial operations and by Monte Carlo simulations. From that structure, specific solutions - as case studies - are developed for the spatial join and skyline. Theoretical and experimental results demonstrated the potential of the developed solutions to meet the threerequirements established in this work.
Detecção da direcionalidade do movimento humano utilizando perturbações do sinal eletromagnético de interfaces IEEE 802.11
(Universidade Federal de Goiás, 2018-10-08) Silva, Bruno Soares da; Laureano, Gustavo Teodoro; http://lattes.cnpq.br/4418446095942420; Abousheaisha, Abdallah S. Abdallah; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Cardoso, Kleber Vieira; Laureano, Gustavo Teodoro; Abousheaisha, Abdallah S. Abdallah; Soares, Anderson da Silva; Viana, Aline Carneiro
The movement flow detection in indoor environments requires the aquisition and implantation of specialized devices. The perturbations that can affect the electromagnetic signals used by 802.11 interfaces make this type of device a low-cost and widely available movement sensor. Most indoor environments have a 802.11 interface, which makes the use of this type of devices a good option as it doesn't requires any new device. In this work, we propose the WiDMove, a proposal to detect the movement flows in an indoor environment using the channel quality measurements (known as Channel State Information - CSI) offered by the IEEE 802.11n standard. Our proposal is based on signal processing and pattern recognition techniques, which allow us to extract and classify event signatures using the CSI. In lab tests with off-the-shelf 802.11 interfaces, we collected CSI samples that were affected by 8 different people. From this collected data we extracted the signature of the entry and exit events using some techniques such as Principal Component Analysis (PCA), Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT). We trained two model types, the first based on a Support Vector Machine (SVM) classifier and the second based on a Multi Layer Perceptral (MLP) neural network. We validated this models with average accuracy experiments and with the cross-validation, including the K-Fold and Leave-One-Out techniques. WiDMove presented that can reach an average accuracy above 93% and that we can train neural networks that can reach an accuracy above 97%.
Detecção de anomalias em aplicações Web utilizando filtros baseados em coeficiente de correlação parcial
(Universidade Federal de Goiás, 2014-10-31) Silva, Otto Julio Ahlert Pinno da; Corrêa, Sand Luz; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Cardoso, Kleber Vieira; Corrêa, Sand Luz; Rodrigues, Vagner José do Sacramento; Santos, Aldri Luiz dos
Finding faults or causes of performance problems in modernWeb computer systems is an arduous task that involves many hours of system metrics monitoring and log analysis. In order to aid administrators in this task, many anomaly detection mechanisms have been proposed to analyze the behavior of the system by collecting a large volume of statistical information showing the condition and performance of the computer system. One of the approaches adopted by these mechanism is the monitoring through strong correlations found in the system. In this approach, the collection of large amounts of data generate drawbacks associated with communication, storage and specially with the processing of information collected. Nevertheless, few mechanisms for detecting anomalies have a strategy for the selection of statistical information to be collected, i.e., for the selection of monitored metrics. This paper presents three metrics selection filters for mechanisms of anomaly detection based on monitoring of correlations. These filters were based on the concept of partial correlation technique which is capable of providing information not observable by common correlations methods. The validation of these filters was performed on a scenario of Web application, and, to simulate this environment, we use the TPC-W, a Web transactions Benchmark of type E-commerce. The results from our evaluation shows that one of our filters allowed the construction of a monitoring network with 8% fewer metrics that state-of-the-art filters, and achieve fault coverage up to 10% more efficient.
Rede de acesso virtualizada: alocação e posicionamento de recursos
(Universidade Federal de Goiás, 2018-10-05) Souza, Phelipe Alves de; Bueno, Elivelton Ferreira; http://lattes.cnpq.br/2764240045623948; Abousheaisha, Abdallah S. Abdallah; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Cardoso, Kleber Vieira; Bueno, Elivelton Ferreira; Abousheaisha, Abdallah S. Abdallah; Pinto, Leizer de Lima; Klautau Júnior, Aldebaro Barreto da Rocha
There are great expectations in CRAN and network virtualization (NFV) technologies, and especially in view of the potential they have to accelerate the deployment of new services while lowering the costs of network operators. Several papers discussed the benefits of deploying a new network infrastructure with such technologies, but only a few investigated how the transition from a legacy network could be. In this context, there is a relevant problem that involves three main issues: 1) which network locations should be updated; 2) how to update the selected location, \ie, to fully virtualized or not; and 3) who should attend virtualized sites. These issues are influenced by the level of centralization employed in a given access network (RAN). Here we propose two optimization models and two heuristics that allow the decision maker to define the desired level of centralization and to evaluate its impact on some metrics such as the investment needed and the level of centralization actually achieved. The models show how the investment should be applied according to the level of centralization and the relative cost between the different resources. Our heuristics present similar performance to the exact approach for relatively small scenarios of the problem, but are able to solve topologies of networks with large number of vertices and maintain a satisfactory solution close to the ideal.

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