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Intrusion detection and protection



Systems and users are very vulnerable to attacks of different typology and impact: virus, trojans, ransomware, spyware, data leakage, etc. This way, a principal topic addressed by NESG is that of protecting network environments by means of two subsequent procedures: detection of malicious behaviors, and adoption of countermeasures.

For that, specific research lines are:

  • - Behavior modeling and classification (multivariate, HMM, clustering, SVM, GA, ...)
  • - Anomaly detection
  • - Malware detection and classification
  • - Countermeasures and response mechanisms




  • Goméz Hernández, J. A., García-Teodoro, P., Holgado-Terriza, J. A., Maciá-Fernández, G., Camacho, J. & Robles Carrillo, M (2021). AMon: A Monitoring Multidimensional Feature Application to Secure Android Environments. In, pages 31-36. [More] 
  • Sebé, J. M. () (2021). Lı́neas de Defensa y Seguridad en Redes ad hoc: un Estudio Sistemático. In Sebé, J. M. (editor), Actas de la XVI Reunión Española sobre Criptologı́a y Seguridad de la Información (RECSI). [More] 
  • Soufiane, S., Magán-Carrión, R., Medina-Bulo, I. & Bouden, H. (2021). Preserving authentication and availability security services through Multivariate Statistical Network Monitoring. Journal of Information Security and Applications, 58, 102785. [More] 
  • Ruiz-Zafra, A. & Magán-Carrión, R (2020). A Distributed Digital Object Architecture to Support Secure IoT Ecosystems. In Dorronsoro, Bernabé, Ruiz, Patricia, Torre, ., Carlos, J. et al (editors), Optimization and Learning, pages 195-205. Cham : Springer International Publishing. [More] 
  • Magán-Carrión, R., Urda, D., Díaz-Cano, I. & Dorronsoro, B. (2020). Towards a Reliable Comparison and Evaluation of Network Intrusion Detection Systems Based on Machine Learning Approaches. Applied Sciences, 10(5). [More]