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You searched for +publisher:"Universidade Federal do Amazonas" +contributor:("Souza, Jose Neuman de"). Showing records 1 – 3 of 3 total matches.

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1. Souza, Camilo Batista de. Dissemina??o de mensagens em redes oportunistas baseada em rela??es sociais e aprendizagem de m?quina.

Degree: 2019, Universidade Federal do Amazonas

Redes Oportunistas est?o se tornando uma solu??o para fornecer suporte de comunica??o em ?reas com redes celulares sobrecarregadas, e em cen?rios onde uma infraestrutura fixa n?o est? dispon?vel, como em regi?es remotas e em desenvolvimento. Uma quest?o cr?tica, que ainda requer uma solu??o satisfat?ria, ? o projeto de uma solu??o eficiente de dissemina??o de dados em termos da taxa de entrega, atraso m?dio e custo de encaminhamentos. Para solucionar esse problema, a maioria dos pesquisadores tem usado o estado da rede ou a mobilidade dos n?s como um crit?rio para a dissemina??o dos dados. Recentemente, solu??es baseadas em relacionamentos sociais t?m sido consideradas como uma alternativa promissora. Seguindo a filosofia dessa nova categoria de protocolos, na presente tese de doutorado apresentam-se dois algoritmos para Redes Oportunistas, os quais tomam suas decis?es de roteamento e gerenciamento de recursos considerando os la?os sociais entre os n?s da rede. Para o problema do roteamento, apresenta-se o algoritmo Friendship and Selfishness Forwarding Quando surge uma oportunidade de contato, o algoritmo proposto primeiramente classifica os la?os sociais entre o destinat?rio da mensagem e o n? candidato a receber a mensagem, doravante referido como relay. Posteriormente, utilizando fun??es log?sticas, o algoritmo proposto avalia o ego?smo do n? relay para considerar os casos em que o n? receptor ? ego?sta seja porque seu dispositivo est? com limita??es de recursos, ou porque ele ? racionalmente ego?sta. Para o problema do gerenciamento de buffer, ? introduzido o algoritmo denominado Friendly-drop (FDA), o qual toma suas decis?es de encaminhamento/descarte de mensagens baseando-se nos relacionamentos sociais entre os n?s. Quando o buffer dos n?s est? cheio, FDA prioriza o descarte de mensagens destinadas a usu?rios com quem a rela??o social ? mais fraca. Por outro lado, quando os n?s est?o em contato, FDA prioriza o envio de mensagens destinadas a usu?rios com quem a rela??o social ? mais forte. Os resultados obtidos atrav?s do simulador The ONE mostram que, mesmo considerando o ego?smo dos n?s no problema de dissemina??o de mensagens, o algoritmo proposto supera outros algoritmos bem conhecidos na literatura, aumentando a taxa de entrega em at? 20% e com a vantagem de precisar de um menor n?mero de eventos de encaminhamento. Os resultados obtidos na presente tese de doutorado tamb?m demonstram que o algoritmo de gerenciamento de buffer pode se tornar uma importante chave para melhorar o desempenho da rede em cen?rios com n?s ego?stas.

Opportunistic networks provide communication support in areas with overloaded cellular networks, and in scenarios where a fixed infrastructure is not available, as in remote and developing regions. A critical issue, which still requires a satisfactory solution, is the design of an efficient data delivery solution that considers delivery efficiency, delay, and cost. To tackle this problem, most researchers have used either the network state or node…

Advisors/Committee Members: Mota, Edjair de Souza, 57184399234, http://lattes.cnpq.br/5771638576099195, Carvalho, Leandro Silva Galv?o de, http://lattes.cnpq.br/6049960144667044, Carvalho, Celso Barbosa, http://lattes.cnpq.br/8269546823033896, Souza, Jose Neuman de, http://lattes.cnpq.br/3614256141054800, Calafate, Carlos Tavares, [email protected].

Subjects/Keywords: Aprendizado do computador; Redes de computadores; CI?NCIAS EXATAS E DA TERRA: CI?NCIA DA COMPUTA??O; Aprendizagem de m?quina; Redes de computadores; Relacionamentos sociais

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Souza, C. B. d. (2019). Dissemina??o de mensagens em redes oportunistas baseada em rela??es sociais e aprendizagem de m?quina. (Doctoral Dissertation). Universidade Federal do Amazonas. Retrieved from https://tede.ufam.edu.br/handle/tede/7478

Chicago Manual of Style (16th Edition):

Souza, Camilo Batista de. “Dissemina??o de mensagens em redes oportunistas baseada em rela??es sociais e aprendizagem de m?quina.” 2019. Doctoral Dissertation, Universidade Federal do Amazonas. Accessed August 03, 2020. https://tede.ufam.edu.br/handle/tede/7478.

MLA Handbook (7th Edition):

Souza, Camilo Batista de. “Dissemina??o de mensagens em redes oportunistas baseada em rela??es sociais e aprendizagem de m?quina.” 2019. Web. 03 Aug 2020.

Vancouver:

Souza CBd. Dissemina??o de mensagens em redes oportunistas baseada em rela??es sociais e aprendizagem de m?quina. [Internet] [Doctoral dissertation]. Universidade Federal do Amazonas; 2019. [cited 2020 Aug 03]. Available from: https://tede.ufam.edu.br/handle/tede/7478.

Council of Science Editors:

Souza CBd. Dissemina??o de mensagens em redes oportunistas baseada em rela??es sociais e aprendizagem de m?quina. [Doctoral Dissertation]. Universidade Federal do Amazonas; 2019. Available from: https://tede.ufam.edu.br/handle/tede/7478

2. Fonseca, Paulo C?sar da Rocha; http://lattes.cnpq.br/3639575844521754. A deep learning framework for BGP anomaly detection and classification.

Degree: 2019, Universidade Federal do Amazonas

The Border Gateway Protocol (BGP) is the default Internet routing protocol that manages connectivity among Autonomous Systems (ASes). Although BGP disruptions are rare when they occur the consequences can be very damaging. Consequently, there has been a considerable effort aimed at understanding what is normal and abnormal BGP traffic and, in so doing, enable potentially disruptive anomalous traffic to be identified quickly. Even though there is an extensive research on anomaly detection, there are two major gaps in current literature: the scarcity of public datasets for all types of events and the lack of a BGP anomaly classification framework that differentiates anomaly classes. Since that there are no public datasets of labeled BGP anomalous events, each model was validated using different datasets, which had to be individually generated for each approach. The absence of common groundwork dataset increases the difficulty in comparing different approaches. The lack of a classification framework hinders the deployment of specific mitigation measures to each anomaly class in an automated fashion. In the current work, we address both problems: 1) We provide a BGP dataset generation tool and publicly available datasets for different anomaly classes. These datasets contain the most used features by previous research efforts and additional novel features; 2) We address the BGP anomaly classification problem by developing a framework that uses deep learning as the core engine of an anomaly detection and classification mechanism. We built a model that exploits different neural network architectures advantages. Both novel features and the BGP anomaly detector and classifier were evaluated and it was demonstrated that they can be used to react to anomalies in real-time and leverage the deployment of different mitigation and coordination strategies to different anomaly classes in an autonomous fashion.

The Border Gateway Protocol (BGP) is the default Internet routing protocol that manages connectivity among Autonomous Systems (ASes). Although BGP disruptions are rare when they occur the consequences can be very damaging. Consequently, there has been a considerable effort aimed at understanding what is normal and abnormal BGP traffic and, in so doing, enable potentially disruptive anomalous traffic to be identified quickly. Even though there is an extensive research on anomaly detection, there are two major gaps in current literature: the scarcity of public datasets for all types of events and the lack of a BGP anomaly classification framework that differentiates anomaly classes. Since that there are no public datasets of labeled BGP anomalous events, each model was validated using different datasets, which had to be individually generated for each approach. The absence of common groundwork dataset increases the difficulty in comparing different approaches. The lack of a classification framework hinders the deployment of specific mitigation measures to each anomaly class in an automated fashion. In the current work, we…

Advisors/Committee Members: Mota, Edjard Souza, http://lattes.cnpq.br/0757666181169076, Feitosa, Eduardo Luzeiro, http://lattes.cnpq.br/5939944067207881, Carvalho, Andr? Luiz da Costa, http://lattes.cnpq.br/4863447798119856, Souza, Jose Neuman de, http://lattes.cnpq.br/3614256141054800.

Subjects/Keywords: Border Gateway Protocol; Machine Learning; Dataset generation; Autonomous Systems; Anomalias BGP; CI?NCIAS EXATAS E DA TERRA: CI?NCIA DA COMPUTA??O; Border Gateway Protocol; Anomaly detection; Machine Learning; Dataset generation; Detec??o de anomalias

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Fonseca, P. C. d. R. h. c. b. (2019). A deep learning framework for BGP anomaly detection and classification. (Doctoral Dissertation). Universidade Federal do Amazonas. Retrieved from https://tede.ufam.edu.br/handle/tede/7700

Chicago Manual of Style (16th Edition):

Fonseca, Paulo C?sar da Rocha; http://lattes cnpq br/3639575844521754. “A deep learning framework for BGP anomaly detection and classification.” 2019. Doctoral Dissertation, Universidade Federal do Amazonas. Accessed August 03, 2020. https://tede.ufam.edu.br/handle/tede/7700.

MLA Handbook (7th Edition):

Fonseca, Paulo C?sar da Rocha; http://lattes cnpq br/3639575844521754. “A deep learning framework for BGP anomaly detection and classification.” 2019. Web. 03 Aug 2020.

Vancouver:

Fonseca PCdRhcb. A deep learning framework for BGP anomaly detection and classification. [Internet] [Doctoral dissertation]. Universidade Federal do Amazonas; 2019. [cited 2020 Aug 03]. Available from: https://tede.ufam.edu.br/handle/tede/7700.

Council of Science Editors:

Fonseca PCdRhcb. A deep learning framework for BGP anomaly detection and classification. [Doctoral Dissertation]. Universidade Federal do Amazonas; 2019. Available from: https://tede.ufam.edu.br/handle/tede/7700

3. Silva, Ricardo Bennesby da. DeepBGP: A Machine Learning Solution to reduce BGP Routing Convergence Time by Fine-Tuning MRAI.

Degree: 2019, Universidade Federal do Amazonas

The organization of the Internet is composed of administrative domains, known as Autonomous Systems (ASes), that exchange reachability information by means of the Border Gateway Protocol (BGP). Since a high convergence delay leads to packet losses and service unavailability, such a protocol has to converge as fast as possible. As this can happen due to BGP's own mechanism of UPDATE messages, that produces a humongous amount of messages, BGP reduces the number of UPDATEs exchanged between two BGP routers by holding consecutive announcements from a router to a neighbor for a given amount of time. The BGP timer responsible for this task is called Minimum Route Advertisement Interval (MRAI), which has an important impact in routing convergence. The Software-Defined Networking (SDN) paradigm can be used to leverage interdomain routing services performance via the logically centralized controlling benefits of intradomain settings. SDN principles has been successfully deployed in data centers, LANs, and in several other studies, where each each AS is modeled with a logically centralized routing control, offering new opportunities and bringing BGP routing convergence improvements. In this work, an extensive survey is presented on the state-of-the-art about research efforts to achieve better BGP routing convergence time. Furthermore, I pinpoint the open issues in this research field and propose DeepBGP, to the best of my knowledge, the first hybrid framework endowed with a learning mechanism, that integrates the SDN paradigm within interdomain routing domains, to improve the interdomain routing convergence time. This is achieved by employing the LSTM learning technique that allows the tuning of MRAI value aiming to reduce the convergence time according to learned patterns from collected BGP UPDATE features. The PEERING platform was used to provide a real scenario that allows the sending of announcements to the Internet. With the benefits of having such an actual testbed I carried out experiments with protocol characteristics that can impact the routing convergence. The experimental results show that the adaptive MRAI in the DeepBGP framework is able to reduce the BGP routing convergence time when compared to the use of static MRAIs.

The organization of the Internet is composed of administrative domains, known as Autonomous Systems (ASes), that exchange reachability information by means of the Border Gateway Protocol (BGP). Since a high convergence delay leads to packet losses and service unavailability, such a protocol has to converge as fast as possible. As this can happen due to BGP's own mechanism of UPDATE messages, that produces a humongous amount of messages, BGP reduces the number of UPDATEs exchanged between two BGP routers by holding consecutive announcements from a router to a neighbor for a given amount of time. The BGP timer responsible for this task is called Minimum Route Advertisement Interval (MRAI), which has an important impact in routing convergence. The Software-Defined Networking (SDN)…

Advisors/Committee Members: Mota, Edjard Souza, http://lattes.cnpq.br/0757666181169076, Feitosa, Eduardo Luzeiro, http://lattes.cnpq.br/5939944067207881, Santos, Eulanda Miranda dos, http://lattes.cnpq.br/3054990742969890, Souza, Jose Neuman de, http://lattes.cnpq.br/3614256141054800.

Subjects/Keywords: Gerenciamento de redes; Roteamento entre dom?nios; Tempo de converg?ncia; Border Gateway Protocol; Long Short-Term Memory; Long Short-Term Memory; CI?NCIAS EXATAS E DA TERRA: CI?NCIA DA COMPUTA??O: SISTEMAS DE COMPUTA??O; bgp; convergence time; lstm; network management; interdomain routing

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Silva, R. B. d. (2019). DeepBGP: A Machine Learning Solution to reduce BGP Routing Convergence Time by Fine-Tuning MRAI. (Doctoral Dissertation). Universidade Federal do Amazonas. Retrieved from https://tede.ufam.edu.br/handle/tede/7697

Chicago Manual of Style (16th Edition):

Silva, Ricardo Bennesby da. “DeepBGP: A Machine Learning Solution to reduce BGP Routing Convergence Time by Fine-Tuning MRAI.” 2019. Doctoral Dissertation, Universidade Federal do Amazonas. Accessed August 03, 2020. https://tede.ufam.edu.br/handle/tede/7697.

MLA Handbook (7th Edition):

Silva, Ricardo Bennesby da. “DeepBGP: A Machine Learning Solution to reduce BGP Routing Convergence Time by Fine-Tuning MRAI.” 2019. Web. 03 Aug 2020.

Vancouver:

Silva RBd. DeepBGP: A Machine Learning Solution to reduce BGP Routing Convergence Time by Fine-Tuning MRAI. [Internet] [Doctoral dissertation]. Universidade Federal do Amazonas; 2019. [cited 2020 Aug 03]. Available from: https://tede.ufam.edu.br/handle/tede/7697.

Council of Science Editors:

Silva RBd. DeepBGP: A Machine Learning Solution to reduce BGP Routing Convergence Time by Fine-Tuning MRAI. [Doctoral Dissertation]. Universidade Federal do Amazonas; 2019. Available from: https://tede.ufam.edu.br/handle/tede/7697

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