Roberto Magán‐Carrión

607 total citations
18 papers, 368 citations indexed

About

Roberto Magán‐Carrión is a scholar working on Computer Networks and Communications, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Roberto Magán‐Carrión has authored 18 papers receiving a total of 368 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Networks and Communications, 12 papers in Artificial Intelligence and 5 papers in Signal Processing. Recurrent topics in Roberto Magán‐Carrión's work include Network Security and Intrusion Detection (11 papers), Anomaly Detection Techniques and Applications (8 papers) and Advanced Malware Detection Techniques (5 papers). Roberto Magán‐Carrión is often cited by papers focused on Network Security and Intrusion Detection (11 papers), Anomaly Detection Techniques and Applications (8 papers) and Advanced Malware Detection Techniques (5 papers). Roberto Magán‐Carrión collaborates with scholars based in Spain, Switzerland and Qatar. Roberto Magán‐Carrión's co-authors include Pedro García‐Teodoro, José Camacho, Gabriel Maciá‐Fernández, Roberto Therón, Daniel Urda, Bernabè Dorronsoro, Rafael A. Rodríguez‐Gómez, Gianni A. Di, Eduardo Feo Flushing and Ignácio Cano and has published in prestigious journals such as Sensors, Applied Sciences and Computer Networks.

In The Last Decade

Roberto Magán‐Carrión

17 papers receiving 357 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Roberto Magán‐Carrión Spain 9 315 196 132 49 41 18 368
Rafael A. Rodríguez‐Gómez Spain 10 189 0.6× 119 0.6× 101 0.8× 49 1.0× 38 0.9× 21 321
Siyang Zhou China 6 221 0.7× 296 1.5× 124 0.9× 41 0.8× 36 0.9× 14 414
Han Han China 7 193 0.6× 154 0.8× 98 0.7× 33 0.7× 34 0.8× 27 309
Carlos Catania Argentina 9 340 1.1× 311 1.6× 186 1.4× 19 0.4× 60 1.5× 30 436
Wenjuan Wang China 8 197 0.6× 144 0.7× 80 0.6× 33 0.7× 42 1.0× 28 297
Chuanhuang Li China 7 465 1.5× 304 1.6× 218 1.7× 62 1.3× 41 1.0× 40 514
Min Qin United States 10 332 1.1× 138 0.7× 82 0.6× 93 1.9× 49 1.2× 17 399
Hassan Musafer United States 5 257 0.8× 243 1.2× 126 1.0× 25 0.5× 22 0.5× 9 312
Kallol Krishna Karmakar Australia 10 248 0.8× 84 0.4× 60 0.5× 54 1.1× 62 1.5× 27 295
Awais Aziz Shah United Kingdom 8 245 0.8× 160 0.8× 119 0.9× 22 0.4× 57 1.4× 18 297

Countries citing papers authored by Roberto Magán‐Carrión

Since Specialization
Citations

This map shows the geographic impact of Roberto Magán‐Carrión'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 Roberto Magán‐Carrión with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roberto Magán‐Carrión more than expected).

Fields of papers citing papers by Roberto Magán‐Carrión

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Roberto Magán‐Carrión. 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 Roberto Magán‐Carrión. The network helps show where Roberto Magán‐Carrión may publish in the future.

Co-authorship network of co-authors of Roberto Magán‐Carrión

This figure shows the co-authorship network connecting the top 25 collaborators of Roberto Magán‐Carrión. A scholar is included among the top collaborators of Roberto Magán‐Carrión 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 Roberto Magán‐Carrión. Roberto Magán‐Carrión is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Morente-Molinera, Juan Antonio, et al.. (2025). Evaluation of Explainable, Interpretable and Non-Interpretable Algorithms for Cyber Threat Detection. Electronics. 14(15). 3073–3073.
2.
Magán‐Carrión, Roberto, et al.. (2024). Methodology for the Detection of Contaminated Training Datasets for Machine Learning-Based Network Intrusion-Detection Systems. Sensors. 24(2). 479–479. 2 indexed citations
3.
Magán‐Carrión, Roberto, et al.. (2023). Crypto-Ransomware: A Revision of the State of the Art, Advances and Challenges. Electronics. 12(21). 4494–4494. 11 indexed citations
4.
Magán‐Carrión, Roberto, et al.. (2022). Drawing the web structure and content analysis beyond the Tor darknet: Freenet as a case of study. Journal of Information Security and Applications. 68. 103229–103229. 4 indexed citations
5.
Magán‐Carrión, Roberto, et al.. (2021). Improving the Reliability of Network Intrusion Detection Systems through Dataset Integration. arXiv (Cornell University). 8 indexed citations
6.
Magán‐Carrión, Roberto, et al.. (2021). Preserving authentication and availability security services through Multivariate Statistical Network Monitoring. Journal of Information Security and Applications. 58. 102785–102785. 3 indexed citations
7.
Magán‐Carrión, Roberto, et al.. (2020). Towards a Reliable Comparison and Evaluation of Network Intrusion Detection Systems Based on Machine Learning Approaches. Applied Sciences. 10(5). 1775–1775. 75 indexed citations
8.
Magán‐Carrión, Roberto & Ignácio Cano. (2019). Evaluación de algoritmos de clasificación para la detección de ataques en red sobre conjuntos de datos reales: UGR’16 dataset como caso de estudio. Dialnet (Universidad de la Rioja). 46–52. 1 indexed citations
9.
Maciá‐Fernández, Gabriel, et al.. (2017). UGR’16: Un nuevo conjunto de datos para la evaluación de IDS de red. RiuNet (Politechnical University of Valencia). 71–78. 2 indexed citations
10.
Magán‐Carrión, Roberto, José Camacho, Pedro García‐Teodoro, Eduardo Feo Flushing, & Gianni A. Di. (2017). A Dynamical Relay node placement solution for MANETs. Computer Communications. 114. 36–50. 21 indexed citations
11.
12.
Maciá‐Fernández, Gabriel, José Camacho, Roberto Magán‐Carrión, Pedro García‐Teodoro, & Roberto Therón. (2017). UGR‘16: A new dataset for the evaluation of cyclostationarity-based network IDSs. Computers & Security. 73. 411–424. 131 indexed citations
13.
Camacho, José, et al.. (2016). Networkmetrics: multivariate big data analysis in the context of the internet. Journal of Chemometrics. 30(9). 488–505. 6 indexed citations
14.
Camacho, José, et al.. (2016). Networkmetrics: multivariate big data analysis in the context of the internet. Journal of Chemometrics. 30(9). 487–487. 1 indexed citations
15.
Magán‐Carrión, Roberto, Rafael A. Rodríguez‐Gómez, José Camacho, & Pedro García‐Teodoro. (2016). Optimal relay placement in multi-hop wireless networks. Ad Hoc Networks. 46. 23–36. 43 indexed citations
16.
Maciá‐Fernández, Gabriel, et al.. (2015). A model of data forwarding in MANETs for lightweight detection of malicious packet dropping. Computer Networks. 87. 44–58. 26 indexed citations
17.
Magán‐Carrión, Roberto, José Camacho, & Pedro García‐Teodoro. (2015). Multivariate Statistical Approach for Anomaly Detection and Lost Data Recovery in Wireless Sensor Networks. International Journal of Distributed Sensor Networks. 11(6). 672124–672124. 18 indexed citations
18.
Magán‐Carrión, Roberto, et al.. (2013). Tampered Data Recovery in WSNs through Dynamic PCA and Variable Routing Strategies. Journal of Communications. 8(11). 738–750. 3 indexed citations

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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