• Intrusion detection and protection
• Data processing and networkmetrics

Rafael A. Rodríguez Gómez is a Ph.D. in the Department of Signal Theory, Telematics and Communications at the University of Granada. In this University he studied Telecommunication Engineering between 2003 and 2008. After that, he received his M.Sc. degree in Multimedia Technologies in 2009. In 2014 he received his Ph.D. titled: "P2P networks protection through traffic analysis".

Nowadays, his research interest is focused on network security, more specifically on P2P networks, the P2P traffic analysis as well the detection and defense against the attacks related with these networks. Currently, to be more precise, he is working in the detection and defense against P2P botnets.

• Rodríguez-Gómez, R. A., Casares-Andrés, A. & Maciá-Fernández, G. (2017). On Understanding the Existence of a Deep Torrent. IEEE Communications Magazine, . [More] 
• Maciá-Fernández, G., Camacho, J., García-Teodoro, P. & Rodríguez-Gómez, R. A (2017). A review of: Hierarchical PCA-Based Multivariate Statistical Network Monitoring for Anomaly Detection. In. [More] 
• Camacho, J., Rodríguez-Gómez, R. A. & Saccenti, E. (2017). Group-wise Principal Component Analysis for Exploratory Data Analysis. Journal of Computational and Graphical Statistics, 26(3), 501-512. [More] 
• Magán-Carrión, R., Rodríguez-Gómez, R. A., Camacho, J. & García-Teodoro, P. (2016). Optimal Relay Placement in Multi-Hop Wireless Networks. Ad Hoc Networks (Elsevier), 46, 23-36. [More] 
• Maciá-Fernández, G., Camacho, J., García-Teodoro, P. & Rodríguez-Gómez, R. A (2016). Hierarchical PCA-Based Multivariate Statistical Network Monitoring for Anomaly Detection. In 8th IEEE International Workshop on Information Forensics and Security (WIFS), Abu Dhabi (UAE). [More] 

• VERITAS: Smart Visualization of Network Events for Security Treatment and Analysis
• Information Leakage Detection in the Deep Web
• Security Audit for Grupo Trevenque
• SuMA: Survivability of MANET networks under security incidents
• Detection of anomalous web-user behavior in financial applications