Mário Lemes Proença

2.6k total citations
78 papers, 1.8k citations indexed

About

Mário Lemes Proença is a scholar working on Computer Networks and Communications, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Mário Lemes Proença has authored 78 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 68 papers in Computer Networks and Communications, 45 papers in Artificial Intelligence and 20 papers in Signal Processing. Recurrent topics in Mário Lemes Proença's work include Network Security and Intrusion Detection (57 papers), Anomaly Detection Techniques and Applications (30 papers) and Internet Traffic Analysis and Secure E-voting (25 papers). Mário Lemes Proença is often cited by papers focused on Network Security and Intrusion Detection (57 papers), Anomaly Detection Techniques and Applications (30 papers) and Internet Traffic Analysis and Secure E-voting (25 papers). Mário Lemes Proença collaborates with scholars based in Brazil, Portugal and Spain. Mário Lemes Proença's co-authors include Luiz F. Carvalho, Joel J. P. C. Rodrigues, Jaime Lloret, Marcos V. O. de Assis, Taufik Abrão, Gilberto Fernandes, Lucas Dias Hiera Sampaio, Jalal Al‐Muhtadi, Leonardo de Souza Mendes and Sylvio Barbon and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and IEEE Access.

In The Last Decade

Mário Lemes Proença

70 papers receiving 1.7k citations

Peers

Mário Lemes Proença
Gabriel Maciá‐Fernández Spain
Youjian Zhao China
Animesh Patcha United States
Pedro García‐Teodoro Spain
Pietro Michiardi France
Monowar Bhuyan Sweden
Ikkyun Kim South Korea
Hervé Debar France
Jesús E. Dı́az-Verdejo Spain
Changzhen Hu China
Gabriel Maciá‐Fernández Spain
Mário Lemes Proença
Citations per year, relative to Mário Lemes Proença Mário Lemes Proença (= 1×) peers Gabriel Maciá‐Fernández

Countries citing papers authored by Mário Lemes Proença

Since Specialization
Citations

This map shows the geographic impact of Mário Lemes Proença's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mário Lemes Proença with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mário Lemes Proença more than expected).

Fields of papers citing papers by Mário Lemes Proença

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mário Lemes Proença. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mário Lemes Proença. The network helps show where Mário Lemes Proença may publish in the future.

Co-authorship network of co-authors of Mário Lemes Proença

This figure shows the co-authorship network connecting the top 25 collaborators of Mário Lemes Proença. A scholar is included among the top collaborators of Mário Lemes Proença based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mário Lemes Proença. Mário Lemes Proença is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Carvalho, Luiz F., et al.. (2025). f-AnoGAN for Unsupervised Attack Detection in SDN Environment. IEEE Transactions on Network Science and Engineering. 12(4). 3271–3285.
2.
Carvalho, Luiz F., et al.. (2024). Generative adversarial networks to detect intrusion and anomaly in IP flow-based networks. Future Generation Computer Systems. 163. 107531–107531. 1 indexed citations
3.
Carvalho, Luiz F., et al.. (2024). An Unsupervised Generative Adversarial Network System to Detect DDoS Attacks in SDN. IEEE Access. 12. 70690–70706. 11 indexed citations
4.
Assis, Marcos V. O. de, et al.. (2024). Anomaly and intrusion detection using deep learning for software-defined networks: A survey. Expert Systems with Applications. 256. 124982–124982. 19 indexed citations
5.
Carvalho, Luiz F., et al.. (2024). Generative Adversarial Network Models for Anomaly Detection in Software-Defined Networks. Journal of Network and Systems Management. 32(4). 5 indexed citations
6.
Barbon, Sylvio, et al.. (2023). Multiple voice disorders in the same individual: Investigating handcrafted features, multi-label classification algorithms, and base-learners. Speech Communication. 152. 102952–102952. 8 indexed citations
7.
Carvalho, Luiz F., et al.. (2022). A Gated Recurrent Unit Deep Learning Model to Detect and Mitigate Distributed Denial of Service and Portscan Attacks. IEEE Access. 10. 73229–73242. 16 indexed citations
8.
Carvalho, Luiz F., et al.. (2021). Unsupervised online anomaly detection in Software Defined Network environments. Expert Systems with Applications. 191. 116225–116225. 18 indexed citations
9.
Carvalho, Luiz F., et al.. (2021). Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments. Future Generation Computer Systems. 125. 156–167. 116 indexed citations
10.
Marinello, José Carlos, et al.. (2020). Machine learning‐based models for spectrum sensing in cooperative radio networks. IET Communications. 14(18). 3102–3109. 22 indexed citations
11.
Carvalho, Luiz F., et al.. (2020). Long Short-Term Memory and Fuzzy Logic for Anomaly Detection and Mitigation in Software-Defined Network Environment. IEEE Access. 8. 83765–83781. 95 indexed citations
12.
Dias, João A., Joel J. P. C. Rodrigues, Vasco N. G. J. Soares, et al.. (2020). Network Management and Monitoring Solutions for Vehicular Networks: A Survey. Electronics. 9(5). 853–853. 9 indexed citations
13.
Carvalho, Luiz F., et al.. (2020). Artificial Immune Systems and Fuzzy Logic to Detect Flooding Attacks in Software-Defined Networks. IEEE Access. 8. 100172–100184. 27 indexed citations
14.
Carvalho, Luiz F., et al.. (2019). Wavelet against random forest for anomaly mitigation in software-defined networking. Applied Soft Computing. 80. 138–153. 15 indexed citations
15.
Assis, Marcos V. O. de, et al.. (2018). Fast Defense System Against Attacks in Software Defined Networks. IEEE Access. 6. 69620–69639. 27 indexed citations
16.
Carvalho, Luiz F., Taufik Abrão, Leonardo de Souza Mendes, & Mário Lemes Proença. (2018). An ecosystem for anomaly detection and mitigation in software-defined networking. Expert Systems with Applications. 104. 121–133. 50 indexed citations
17.
Barbon, Sylvio, et al.. (2015). Account classification in online social networks with LBCA and wavelets. Information Sciences. 332. 72–83. 39 indexed citations
18.
Carvalho, Luiz F., et al.. (2015). Using Firefly and Genetic Metaheuristics for Anomaly Detection based on Network Flows. International Conference on Telecommunications. 113–118. 8 indexed citations
19.
Silva, Bruno M., Vasco N. G. J. Soares, Mário Lemes Proença, & Joel J. P. C. Rodrigues. (2010). Impact of content storage and retrieval mechanisms on the performance of Vehicular Delay-Tolerant Networks. Repositório Científico do Instituto Politécnico de Castelo Branco (Instituto Politécnico de Castelo Branco). 407–411. 3 indexed citations
20.
Oliveira, José Palazzo Moreira de, et al.. (2010). Técnicas de Extração de Informação para Avaliação da Qualidade de Páginas Web com o Uso de Ontologias. Lume (Universidade Federal do Rio Grande do Sul). 5(1). 77–88.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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