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Diogo Menezes Ferrazani Mattos is a professor at the Universidade Federal Fluminense (Niterói, Brazil). He received his degrees of D.Sc. and M.Sc. in Electrical Engineering from Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil, in 2017 and 2012. Between 2015 and 2016, he had a sandwich scholarship to work on his Ph.D. thesis on the LIP6 (Laboratoire d’Informatique de Paris 6) at Université Pierre et Marie Curie, Paris, France. He received a Computer and Information Engineer degree from Universidade Federal do Rio de Janeiro, in 2010, awarded with Magna Cum Laude.
The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and credibility in government institutions. In this paper, we survey methods for preprocessing data in natural language, vectorization, dimensionality reduction, machine learning, and quality assessment of information retrieval. We also contextualize the identification of fake news, and we discuss research initiatives and opportunities.
Nicollas de Oliveira; Pedro Pisa; Martin Lopez; Dianne de Medeiros; Diogo Mattos. Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges. Information 2021, 12, 38 .
AMA StyleNicollas de Oliveira, Pedro Pisa, Martin Lopez, Dianne de Medeiros, Diogo Mattos. Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges. Information. 2021; 12 (1):38.
Chicago/Turabian StyleNicollas de Oliveira; Pedro Pisa; Martin Lopez; Dianne de Medeiros; Diogo Mattos. 2021. "Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges." Information 12, no. 1: 38.
A alocação eficiente do tráfego em nuvens é desafiadora devido ao compartilhamento de recursos entre os clientes. Isso pode implicar recursos ociosos caso os clientes estejam limitados a utilizar somente a banda contratada. O uso da nuvem pode ser otimizado provendo recursos aos clientes dinamicamente de acordo com a demanda. Agentes de aprendizado por reforço promovem respostas adaptáveis a ambientes variantes no tempo. Este artigo propõe um mecanismo baseado em Q-learning com múltiplos agentes para gerenciar o acesso aos recursos por cada cliente da nuvem. A proposta é analisada em um ambiente emulado, no qual um controlador é responsável pela alocação de tráfego aos clientes. Os resultados mostram que o mecanismo reduz a ociosidade da nuvem, permitindo que clientes com baixa priorização utilizem a banda disponível, ao mesmo tempo que garante a banda contratada a clientes prioritários. O mecanismo exige baixo comprometimento de processamento total, mesmo variando o número de estados e espaço de ações, ao passo que o custo em memória por agente aumenta, alcançando um máximo de 300 kB para 200 estados e ações.
Reiner Henrique Dos Santos Filho; Diogo Menezes Ferrazani Mattos; Dianne Scherly Varela De Medeiros. Agentes Inteligentes baseados em Aprendizado por Reforço para Alocação Dinâmica de Tráfego em Nuvens. Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC 2020) 2020, 141 -154.
AMA StyleReiner Henrique Dos Santos Filho, Diogo Menezes Ferrazani Mattos, Dianne Scherly Varela De Medeiros. Agentes Inteligentes baseados em Aprendizado por Reforço para Alocação Dinâmica de Tráfego em Nuvens. Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC 2020). 2020; ():141-154.
Chicago/Turabian StyleReiner Henrique Dos Santos Filho; Diogo Menezes Ferrazani Mattos; Dianne Scherly Varela De Medeiros. 2020. "Agentes Inteligentes baseados em Aprendizado por Reforço para Alocação Dinâmica de Tráfego em Nuvens." Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC 2020) , no. : 141-154.
Human inefficiency to distinguish between true and false facts poses fake news as a threat to logical truth, which deteriorates democracy, journalism, and credibility in governmental institutions. In this letter, we propose a computational-stylistic analysis based on natural language processing, efficiently applying machine learning algorithms to detect fake news in texts extracted from social media. The analysis considers news from Twitter, from which approximately 33,000 tweets were collected, assorted between real and proven false. In assessing the quality of detection, 86% accuracy, and 94% precision stand out even employing a dimensional reduction to one-sixth of the number of original features. Our approach introduces a minimum overhead, while it has the potential of providing a high confidence index on discriminating fake from real news.
Nicollas Oliveira; Dianne S. V. Medeiros; Diogo M. F. Mattos. A Sensitive Stylistic Approach to Identify Fake News on Social Networking. IEEE Signal Processing Letters 2020, 27, 1250 -1254.
AMA StyleNicollas Oliveira, Dianne S. V. Medeiros, Diogo M. F. Mattos. A Sensitive Stylistic Approach to Identify Fake News on Social Networking. IEEE Signal Processing Letters. 2020; 27 ():1250-1254.
Chicago/Turabian StyleNicollas Oliveira; Dianne S. V. Medeiros; Diogo M. F. Mattos. 2020. "A Sensitive Stylistic Approach to Identify Fake News on Social Networking." IEEE Signal Processing Letters 27, no. : 1250-1254.
Efficient management of cloud traffic leads to resource sharing among clients. To meet clients' performance restrictions, however, cloud providers adopt strict resource allocation that implies idle resources as clients' bandwidth thresholds are over-provisioned. We propose a dynamic mechanism to provide resources on-demand in a multitenant data center. The mechanism relies on Q-learning multi-agent for managing each client access to the cloud resources. The proposed mechanism either measure the throughput continuously or map the CPU usage into bandwidth usage. We assess our mechanism in an emulated environment, in which a network controller provisions bandwidth. Results show that the mechanism reduces cloud idleness, allowing low-priority clients to use bandwidth while there is idle capacity. Our mechanism incurs low processing overhead, even when the number of states and action space grow, while the memory cost per agent increases, peaking at 300 kB for 200 states and actions. When mapping CPU usage into bandwidth usage, the mechanism achieves fair bandwidth sharing at the cost of small increase on the convergence time to an optimal policy.
Reiner H. Santos Filho; Tadeu N. Ferreira; Diogo Mattos; Dianne S. V. Medeiros. A Lightweight Reinforcement-Learning-Based Mechanism for Bandwidth Provisioning on Multitenant Data Center. 2020 International Conference on Systems, Signals and Image Processing (IWSSIP) 2020, 331 -336.
AMA StyleReiner H. Santos Filho, Tadeu N. Ferreira, Diogo Mattos, Dianne S. V. Medeiros. A Lightweight Reinforcement-Learning-Based Mechanism for Bandwidth Provisioning on Multitenant Data Center. 2020 International Conference on Systems, Signals and Image Processing (IWSSIP). 2020; ():331-336.
Chicago/Turabian StyleReiner H. Santos Filho; Tadeu N. Ferreira; Diogo Mattos; Dianne S. V. Medeiros. 2020. "A Lightweight Reinforcement-Learning-Based Mechanism for Bandwidth Provisioning on Multitenant Data Center." 2020 International Conference on Systems, Signals and Image Processing (IWSSIP) , no. : 331-336.
Consensus mechanisms in blockchain applications allow mistrusting peers to agree on the global state of the chain. Most of the existing consensus mechanisms, however, are constrained by low efficiency and high energy consumption. In this paper, we propose the Blockchain Reputation-Based Consensus (BRBC) mechanism in which a node must have the reputation score higher than a given network trust threshold before being allowed to insert a new block in the chain. A randomly-selected set of judges monitors the behaviour of each node involved in the consensus and updates the node reputation score. Every cooperative behaviour results in a reward, and a non-cooperative or malicious behaviour results in a punishment. BRBC also uses the reputation score to revoke access to nodes with a reputation score below a given threshold. We present a security analysis, and we demonstrate that BRBC resists against a set of known attacks in the blockchain network. Finally, we simulate a blockchain network to assert the mechanism scalability and resilience to malicious actions in various network scenarios and different rates of malicious actions. The results show BRBC to be efficient to expel all nodes that acted with more than 50% of malicious actions.
Marcela T. de Oliveira; Lúcio H.A. Reis; Dianne S.V. Medeiros; Ricardo C. Carrano; Sílvia D. Olabarriaga; Diogo M.F. Mattos. Blockchain reputation-based consensus: A scalable and resilient mechanism for distributed mistrusting applications. Computer Networks 2020, 179, 107367 .
AMA StyleMarcela T. de Oliveira, Lúcio H.A. Reis, Dianne S.V. Medeiros, Ricardo C. Carrano, Sílvia D. Olabarriaga, Diogo M.F. Mattos. Blockchain reputation-based consensus: A scalable and resilient mechanism for distributed mistrusting applications. Computer Networks. 2020; 179 ():107367.
Chicago/Turabian StyleMarcela T. de Oliveira; Lúcio H.A. Reis; Dianne S.V. Medeiros; Ricardo C. Carrano; Sílvia D. Olabarriaga; Diogo M.F. Mattos. 2020. "Blockchain reputation-based consensus: A scalable and resilient mechanism for distributed mistrusting applications." Computer Networks 179, no. : 107367.
Attacks on cyber‐physical systems, such as nuclear and water treatment plants, have physical consequences that impact the lives of thousands of citizens. In such systems, it is mandatory to monitor the field network and detect potential threats before a problem occurs. This work proposes a hybrid approach that orchestrates unsupervised and incremental learning methods to detect threats that impact the control loops in a plant. We use online data processing to identify new attack vectors. We train the online incremental learning method as new attacks arrive. We also apply a one‐class support vector machine to each monitored sensor or actuator to retrieve abnormal behaviors of their closed control loop. The proposed solution orchestrates the outputs from the two machine learning methods and alerts the system operators when it detects a threat. We evaluate the proposal on the Secure Water Treatment testbed dataset, and the results reveal that our proposal detects threats at more than 90% precision and with accuracy higher than 95%.
Lúcio Henrik A. Reis; Andrés Murillo Piedrahita; Sandra Rueda; Natália C. Fernandes; Dianne S. V. Medeiros; Marcelo Dias De Amorim; Diogo M. F. Mattos. Unsupervised and incremental learning orchestration for cyber‐physical security. Transactions on Emerging Telecommunications Technologies 2020, 31, 1 .
AMA StyleLúcio Henrik A. Reis, Andrés Murillo Piedrahita, Sandra Rueda, Natália C. Fernandes, Dianne S. V. Medeiros, Marcelo Dias De Amorim, Diogo M. F. Mattos. Unsupervised and incremental learning orchestration for cyber‐physical security. Transactions on Emerging Telecommunications Technologies. 2020; 31 (7):1.
Chicago/Turabian StyleLúcio Henrik A. Reis; Andrés Murillo Piedrahita; Sandra Rueda; Natália C. Fernandes; Dianne S. V. Medeiros; Marcelo Dias De Amorim; Diogo M. F. Mattos. 2020. "Unsupervised and incremental learning orchestration for cyber‐physical security." Transactions on Emerging Telecommunications Technologies 31, no. 7: 1.
Inferring the quality of service experienced by wireless users is challenging, as network monitoring does not capture the service perception for each user individually. In this paper, we propose an unsupervised machine learning approach to infer the quality of service experienced by wireless users, based on the different usage profiles of a large-scale wireless network. To this end, our approach correlates the usage data of access points, and the summaries of connection flows passing through the access points in the network. Then, we apply the k-means clustering algorithm to infer different network usage profiles. We evaluate our proposed approach to infer QoS on a real large-scale wireless network, and the results show that discriminating the flows into five clusters allows identifying prevalent usage profiles of the degraded state of the network and overload conditions in access points, considering only the flow summaries.
Lucio Henrik A. Reis; Luiz Claudio S. Magalhães; Dianne Scherly V. De Medeiros; Diogo M. F. Mattos. An Unsupervised Approach to Infer Quality of Service for Large-Scale Wireless Networking. Journal of Network and Systems Management 2020, 28, 1228 -1247.
AMA StyleLucio Henrik A. Reis, Luiz Claudio S. Magalhães, Dianne Scherly V. De Medeiros, Diogo M. F. Mattos. An Unsupervised Approach to Infer Quality of Service for Large-Scale Wireless Networking. Journal of Network and Systems Management. 2020; 28 (4):1228-1247.
Chicago/Turabian StyleLucio Henrik A. Reis; Luiz Claudio S. Magalhães; Dianne Scherly V. De Medeiros; Diogo M. F. Mattos. 2020. "An Unsupervised Approach to Infer Quality of Service for Large-Scale Wireless Networking." Journal of Network and Systems Management 28, no. 4: 1228-1247.
Blockchain is a disruptive technology that relies on the distributed nature of the peer-to-peer network while performing an agreement, or consensus, a mechanism to achieve an immutable, global, and consistent registry of all transactions. Thus, a key challenge in developing blockchain solutions is to design the consensus mechanism properly. As a consequence of being a distributed application, any consensus mechanism is restricted to offer two of three properties: consistency, availability, and partition tolerance. In this paper, we survey the main consensus mechanisms on blockchain solutions, and we highlight the properties of each one. Moreover, we differentiate both deterministic and probabilistic consensus mechanisms, and we highlight coordination solutions that facilitate the data distribution on the blockchain, without the need for a sophisticated consensus mechanism.
Gabriel R. Carrara; Leonardo M. Burle; Dianne S. V. Medeiros; Célio Vinicius N. De Albuquerque; Diogo M. F. Mattos. Consistency, availability, and partition tolerance in blockchain: a survey on the consensus mechanism over peer-to-peer networking. Annals of Telecommunications 2020, 75, 163 -174.
AMA StyleGabriel R. Carrara, Leonardo M. Burle, Dianne S. V. Medeiros, Célio Vinicius N. De Albuquerque, Diogo M. F. Mattos. Consistency, availability, and partition tolerance in blockchain: a survey on the consensus mechanism over peer-to-peer networking. Annals of Telecommunications. 2020; 75 (3-4):163-174.
Chicago/Turabian StyleGabriel R. Carrara; Leonardo M. Burle; Dianne S. V. Medeiros; Célio Vinicius N. De Albuquerque; Diogo M. F. Mattos. 2020. "Consistency, availability, and partition tolerance in blockchain: a survey on the consensus mechanism over peer-to-peer networking." Annals of Telecommunications 75, no. 3-4: 163-174.
Extracting knowledge from unstructured data silos, a legacy of old applications, is mandatory for improving the governance of today’s cities and fostering the creation of smart cities. Texts in natural language often compose such data. Nevertheless, the inference of useful information from a linguistic-computational analysis of natural language data is an open challenge. In this paper, we propose a clustering method to analyze textual data employing the unsupervised machine learning algorithms k-means and hierarchical clustering. We assess different vector representation methods for text, similarity metrics, and the number of clusters that best matches the data. We evaluate the methods using a real database of a public record service of security occurrences. The results show that the k-means algorithm using Euclidean distance extracts non-trivial knowledge, reaching up to 93% accuracy in a set of test samples while identifying the 12 most prevalent occurrence patterns.
Nicollas Oliveira; H. A. Lucio Reis; Natalia C. Fernandes; A. M. Carlos Bastos; S. V. Dianne Medeiros; Diogo Mattos. Natural Language Processing Characterization of Recurring Calls in Public Security Services. 2020 International Conference on Computing, Networking and Communications (ICNC) 2020, 1009 -1013.
AMA StyleNicollas Oliveira, H. A. Lucio Reis, Natalia C. Fernandes, A. M. Carlos Bastos, S. V. Dianne Medeiros, Diogo Mattos. Natural Language Processing Characterization of Recurring Calls in Public Security Services. 2020 International Conference on Computing, Networking and Communications (ICNC). 2020; ():1009-1013.
Chicago/Turabian StyleNicollas Oliveira; H. A. Lucio Reis; Natalia C. Fernandes; A. M. Carlos Bastos; S. V. Dianne Medeiros; Diogo Mattos. 2020. "Natural Language Processing Characterization of Recurring Calls in Public Security Services." 2020 International Conference on Computing, Networking and Communications (ICNC) , no. : 1009-1013.
Diogo Menezes Ferrazani Mattos; Francine Krief; Sandra Julieta Rueda. Blockchain and artificial intelligence for network security. Annals of Telecommunications 2020, 75, 101 -102.
AMA StyleDiogo Menezes Ferrazani Mattos, Francine Krief, Sandra Julieta Rueda. Blockchain and artificial intelligence for network security. Annals of Telecommunications. 2020; 75 (3-4):101-102.
Chicago/Turabian StyleDiogo Menezes Ferrazani Mattos; Francine Krief; Sandra Julieta Rueda. 2020. "Blockchain and artificial intelligence for network security." Annals of Telecommunications 75, no. 3-4: 101-102.
The funding information in the original manuscript is incorrect, the correct information should be the below:
Helio N. Cunha Neto; Martin Andreoni Lopez; Natalia C. Fernandes; Diogo M. F. Mattos. Correction to: MineCap: super incremental learning for detecting and blocking cryptocurrency mining on software-defined networking. Annals of Telecommunications 2020, 75, 487 -487.
AMA StyleHelio N. Cunha Neto, Martin Andreoni Lopez, Natalia C. Fernandes, Diogo M. F. Mattos. Correction to: MineCap: super incremental learning for detecting and blocking cryptocurrency mining on software-defined networking. Annals of Telecommunications. 2020; 75 (7-8):487-487.
Chicago/Turabian StyleHelio N. Cunha Neto; Martin Andreoni Lopez; Natalia C. Fernandes; Diogo M. F. Mattos. 2020. "Correction to: MineCap: super incremental learning for detecting and blocking cryptocurrency mining on software-defined networking." Annals of Telecommunications 75, no. 7-8: 487-487.
Covert mining of cryptocurrency implies the use of valuable computing resources and high energy consumption. In this paper, we propose MineCap, a dynamic online mechanism for detecting and blocking covert cryptocurrency mining flows, using machine learning on software-defined networking. The proposed mechanism relies on Spark Streaming for online processing of network flows, and, when identifying a mining flow, it requests the flow blocking to the network controller. We also propose a learning technique called super incremental learning, a variant of the super learner applied to online learning, which takes the classification probabilities of an ensemble of classifiers as features for an incremental learning classifier. Hence, we design an accurate mechanism to classify mining flows that learn with incoming data with an average of 98% accuracy, 99% precision, 97% sensitivity, and 99.9% specificity and avoid concept drift–related issues.
Helio N. Cunha Neto; Martin Andreoni Lopez; Natalia C. Fernandes; Diogo Mattos. MineCap: super incremental learning for detecting and blocking cryptocurrency mining on software-defined networking. Annals of Telecommunications 2020, 75, 121 -131.
AMA StyleHelio N. Cunha Neto, Martin Andreoni Lopez, Natalia C. Fernandes, Diogo Mattos. MineCap: super incremental learning for detecting and blocking cryptocurrency mining on software-defined networking. Annals of Telecommunications. 2020; 75 (3-4):121-131.
Chicago/Turabian StyleHelio N. Cunha Neto; Martin Andreoni Lopez; Natalia C. Fernandes; Diogo Mattos. 2020. "MineCap: super incremental learning for detecting and blocking cryptocurrency mining on software-defined networking." Annals of Telecommunications 75, no. 3-4: 121-131.
Private blockchain applications simplify the network consensus while relying on voting-based consensus mechanisms because these mechanisms present lower computational cost than proof-of-work. The voting-based consensus is adequate to networks where all nodes are known, although it requires a high amount of exchanged messages, proportional to the number of participant nodes. In this paper, we propose a strategy for applying Quality of Service (QoS) techniques to ensure the reliable operation of voting-based consensus mechanisms. The proposed strategy is based on Software Defined Networking (SDN) and aims at reducing the time for the termination of the consensus protocols. The proposal evaluation leans on an emulated environment, using Mininet, where we run the Raft and BFT-SMaRt consensus mechanisms. Results show that the proposal ensures the termination of the protocols in a reduced time and the reduction of the control load for the election of leaders since we assure a minimum quality of service on the throughput rate for the consensus network flows. The proposed strategy enhances up to 100% of the consensus terminations on Raft and, for BFT-SMaRt, ensured the operation even in contention scenarios where the consensus was previously unfeasible. Results also show 80% reduction in the exchanged messages for achieving consensus.
Gabriel R. Carrara; Lucio H. A. Reis; Celio V. N. Albuquerque; Diogo Mattos. A Lightweight Strategy for Reliability of Consensus Mechanisms based on Software Defined Networks. 2019 Global Information Infrastructure and Networking Symposium (GIIS) 2019, 1 -6.
AMA StyleGabriel R. Carrara, Lucio H. A. Reis, Celio V. N. Albuquerque, Diogo Mattos. A Lightweight Strategy for Reliability of Consensus Mechanisms based on Software Defined Networks. 2019 Global Information Infrastructure and Networking Symposium (GIIS). 2019; ():1-6.
Chicago/Turabian StyleGabriel R. Carrara; Lucio H. A. Reis; Celio V. N. Albuquerque; Diogo Mattos. 2019. "A Lightweight Strategy for Reliability of Consensus Mechanisms based on Software Defined Networks." 2019 Global Information Infrastructure and Networking Symposium (GIIS) , no. : 1-6.
The software-defined networking paradigm adds flexibility to network management as it allows the policy application in fined-grained flow level. However, the traditional definition of flow disregards user identification credentials. Thus, Identity Management in software-defined networking is a current challenge. In this paper, we propose an access control architecture for software-defined networking, based on ITU X.812 standard and implemented on AuthFlow authentication framework. The proposed architecture integrates AuthFlow with an attribute repository that maps network policies to user attributes. The proposal supports its integration with identity federation, and we evaluate it under a role-based access control model. The evaluated use case is a service differentiation policy according to the role of each user. The evaluation results demonstrate the correct application of the quality of service according to the role of the flow target user.
Bruno Jose C. De A. Martins; Diogo Mattos; Natalia C. Fernandes; Debora Muchaluat-Saade; Alex Borges Vieira; Edelberto Franco Silva. An Extensible Access Control Architecture for Software Defined Networks based on X.812. 2019 IEEE Latin-American Conference on Communications (LATINCOM) 2019, 1 -6.
AMA StyleBruno Jose C. De A. Martins, Diogo Mattos, Natalia C. Fernandes, Debora Muchaluat-Saade, Alex Borges Vieira, Edelberto Franco Silva. An Extensible Access Control Architecture for Software Defined Networks based on X.812. 2019 IEEE Latin-American Conference on Communications (LATINCOM). 2019; ():1-6.
Chicago/Turabian StyleBruno Jose C. De A. Martins; Diogo Mattos; Natalia C. Fernandes; Debora Muchaluat-Saade; Alex Borges Vieira; Edelberto Franco Silva. 2019. "An Extensible Access Control Architecture for Software Defined Networks based on X.812." 2019 IEEE Latin-American Conference on Communications (LATINCOM) , no. : 1-6.
Andreane Spano Da Roza; Joao Vitor Valle; Diogo Mattos. A Precise Method for Monitoring and Detecting Recurrent Attacks on Wireless Networks based on Link-Layer Traffic Classification. 2019 3rd Cyber Security in Networking Conference (CSNet) 2019, 1 .
AMA StyleAndreane Spano Da Roza, Joao Vitor Valle, Diogo Mattos. A Precise Method for Monitoring and Detecting Recurrent Attacks on Wireless Networks based on Link-Layer Traffic Classification. 2019 3rd Cyber Security in Networking Conference (CSNet). 2019; ():1.
Chicago/Turabian StyleAndreane Spano Da Roza; Joao Vitor Valle; Diogo Mattos. 2019. "A Precise Method for Monitoring and Detecting Recurrent Attacks on Wireless Networks based on Link-Layer Traffic Classification." 2019 3rd Cyber Security in Networking Conference (CSNet) , no. : 1.
The smart grid employs information and communication technologies to improve the management of the electric system. Devices connected to the grid can cause damage to the electric system if they exhibit unpredictable behavior. Electric vehicles are examples of critical devices which do not have a defined time or place to connect to the power grid. Therefore, the smart grid requires a mechanism to identify each electric vehicle and avoid spurious devices, and also providing the correct billing and auditing logs. In this paper, we propose an authentication mechanism grounded on software-defined networks and based on the IEC 61850 standard. The proposed mechanism acts over the data link layer and guarantees the devices' authentication with a low control load. The proposal ensures that only authenticated devices communicate within the smart grid, preventing malicious interference from unauthorized devices. A prototype was developed and evaluated through emulation in Mininet. The test results show low authentication time and low control load even when many electrical vehicles authenticates simultaneously.
Arthur A. Z. Soares; Diogo Mattos; Yona Lopes; Dianne S. V. Medeiros; Natalia C. Fernandes; Debora C. Muchaluat-Saade. An Efficient Authentication Mechanism based on Software-Defined Networks for Electric Vehicles. 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE) 2019, 2471 -2476.
AMA StyleArthur A. Z. Soares, Diogo Mattos, Yona Lopes, Dianne S. V. Medeiros, Natalia C. Fernandes, Debora C. Muchaluat-Saade. An Efficient Authentication Mechanism based on Software-Defined Networks for Electric Vehicles. 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE). 2019; ():2471-2476.
Chicago/Turabian StyleArthur A. Z. Soares; Diogo Mattos; Yona Lopes; Dianne S. V. Medeiros; Natalia C. Fernandes; Debora C. Muchaluat-Saade. 2019. "An Efficient Authentication Mechanism based on Software-Defined Networks for Electric Vehicles." 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE) , no. : 2471-2476.
The late detection of security threats causes a significant increase in the risk of irreparable damages and restricts any defense attempt. In this paper, we propose a sCAlable TRAffic Classifier and Analyzer (CATRACA). CATRACA works as an efficient online Intrusion Detection and Prevention System implemented as a Virtualized Network Function. CATRACA is based on Apache Spark, a Big Data Streaming processing system, and it is deployed over the Open Platform for Network Functions Virtualization (OPNFV), providing an accurate real‐time threat‐detection service. The system presents a friendly graphical interface that provides real‐time visualization of the traffic and the attacks that occur in the network. Our prototype can differentiate normal traffic from denial of service (DoS) attacks and vulnerability probes over 95% accuracy under three different datasets. Moreover, CATRACA handles streaming data under concept drift detection with more than 85% of accuracy.
Martin Andreoni Lopez; Diogo Mattos; Otto Carlos M. B. Duarte; Guy Pujolle. Toward a monitoring and threat detection system based on stream processing as a virtual network function for big data. Concurrency and Computation: Practice and Experience 2019, 31, e5344 .
AMA StyleMartin Andreoni Lopez, Diogo Mattos, Otto Carlos M. B. Duarte, Guy Pujolle. Toward a monitoring and threat detection system based on stream processing as a virtual network function for big data. Concurrency and Computation: Practice and Experience. 2019; 31 (20):e5344.
Chicago/Turabian StyleMartin Andreoni Lopez; Diogo Mattos; Otto Carlos M. B. Duarte; Guy Pujolle. 2019. "Toward a monitoring and threat detection system based on stream processing as a virtual network function for big data." Concurrency and Computation: Practice and Experience 31, no. 20: e5344.
Rida Khatoun; Diogo Mattos; Otto Carlos Muniz Bandeira Duarte. Cybersecurity in networking. Annals of Telecommunications 2019, 74, 123 -124.
AMA StyleRida Khatoun, Diogo Mattos, Otto Carlos Muniz Bandeira Duarte. Cybersecurity in networking. Annals of Telecommunications. 2019; 74 (3-4):123-124.
Chicago/Turabian StyleRida Khatoun; Diogo Mattos; Otto Carlos Muniz Bandeira Duarte. 2019. "Cybersecurity in networking." Annals of Telecommunications 74, no. 3-4: 123-124.
The processing and power-consumption constraints of the Internet of Things devices hinder them to offer more complex network services than the simple data transmission in smart city scenarios. The lack of complex services, such as security and quality of service, can even foster disasters in urban centers. In this paper, we propose the integration of complex network services from the IoT devices till a cloud environment through an agile and effective network function virtualization infrastructure of isolated IoT domains. Therefore, our proposal develops a simple gateway access node that virtualizes the domains to which the devices connect. A prototype for services of security and quality of service has been implemented and its evaluation shows that virtualization of the access node does not impact the performance of virtual network functions. The results also show that the proposal provides security for IoT devices, identifying malicious traffic with 99.8% accuracy, avoiding denial of essential services, and ensuring the quality of service.
Diogo Mattos; Pedro Braconnot Velloso; Otto Carlos Muniz Bandeira Duarte. An agile and effective network function virtualization infrastructure for the Internet of Things. Journal of Internet Services and Applications 2019, 10, 6 .
AMA StyleDiogo Mattos, Pedro Braconnot Velloso, Otto Carlos Muniz Bandeira Duarte. An agile and effective network function virtualization infrastructure for the Internet of Things. Journal of Internet Services and Applications. 2019; 10 (1):6.
Chicago/Turabian StyleDiogo Mattos; Pedro Braconnot Velloso; Otto Carlos Muniz Bandeira Duarte. 2019. "An agile and effective network function virtualization infrastructure for the Internet of Things." Journal of Internet Services and Applications 10, no. 1: 6.
The blockchain is currently under the spotlight of trending technologies. It adds security to private applications in several areas of knowledge, and its versatility results in the development of multiple frameworks to meet the requirements of each application. Thus, it is a key challenge to ensure that blockchain frameworks provide security, access control, and high performance to applications. In this paper, we evaluate two frameworks for blockchain development, Parity, and Multichain, which provide configuration and permission flexibility. Our evaluation is a comprehensive comparison between the frameworks, focusing on the analysis of transaction-validation time, transaction-mining time, transaction-seek time and block-seek time. To this end, we deploy peer-to-peer private permissioned networks, in which the frameworks generate the blockchain applications. For each framework, we provide a realistic workload, based on the distribution of probability of interarrival time of transactions on the Bitcoin network. The results show that each framework stands out under specific criteria, and their design decisions imply restrictions on features that are critical for creating secure and efficient blockchain applications.
Marcela T. Oliveira; Gabriel R. Carrara; Natalia C. Fernandes; Célio Albuquerque; Ricardo C. Carrano; Dianne S. V. Medeiros; Diogo Mattos. Towards a Performance Evaluation of Private Blockchain Frameworks using a Realistic Workload. 2019 22nd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) 2019, 180 -187.
AMA StyleMarcela T. Oliveira, Gabriel R. Carrara, Natalia C. Fernandes, Célio Albuquerque, Ricardo C. Carrano, Dianne S. V. Medeiros, Diogo Mattos. Towards a Performance Evaluation of Private Blockchain Frameworks using a Realistic Workload. 2019 22nd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN). 2019; ():180-187.
Chicago/Turabian StyleMarcela T. Oliveira; Gabriel R. Carrara; Natalia C. Fernandes; Célio Albuquerque; Ricardo C. Carrano; Dianne S. V. Medeiros; Diogo Mattos. 2019. "Towards a Performance Evaluation of Private Blockchain Frameworks using a Realistic Workload." 2019 22nd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) , no. : 180-187.