Results

Journal Papers and Book Chapters

  1. Medina-Arco, J.G , Magán-Carrión, R., Rodríguez-Gómez, R.A., García-Teodoro, P. Methodology for the Detection of Contaminated Training Datasets for Machine Learning-Based Network Intrusion-Detection Systems. Sensors, , 24:479. Open version Published version
  2. García-Peñas, R., Rodríguez-Gómez, R.A., Maciá-Fernández, G. HoDiNT: Distributed architecture for collection and analysis of Internet Background Radiation. Computer Networks, , 250:110570. Open version Published version
  3. Camacho, J., Rodríguez-Gómez, R.A. Data quality tools to optimize an anomaly detection benchmark. Submitted to Data, .
  4. Jamoos, M., Mora, A.M. , AlKhanafseh, M., Surakh, O. A Comparative Analysis of the TDCGAN Model for Data Balancing and Intrusion Detection. Signals, , 5(3):580-596. Open version Published version
  5. Camacho, J., Sorochan Armstrong, M. Population Power Curves in ASCA with Permutation Testing. Journal of Chemometrics, . Open version Published version
  6. Saccenti, E., Timmerman, M.E., Camacho, J. A simulation study of the effects of additive, multiplicative, correlated and uncorrelated error on Principal Components Analysis. Journal of Chemometrics, . Open version Published version
  7. Camacho, J., Wasielewska, K., Bro R., Kotz, D. Interpretable Learning in Multivariate Big Data Analysis for Network Monitoring. IEEE Transactions of Network and Service Management, , 21(3):2926-2943. Open version Published version
  8. Wasielewska, K., Soukup, D., Čejka, T., Camacho, J. Dataset Quality Assessment in Autonomous Networks with Permutation Testing. . Submitted to Applied Intelligence, . Open version
  9. Jamoos, M., Mora, A.M., AlKhanafseh, M., Surakhi, O. A New Data-Balancing Approach Based on Generative Adversarial Network for Network Intrusion Detection System. Electronics, , 12:2851. Open version Published version
  10. Al-Zoubi, A.M., Mora, A.M., Faris, H. A Multilingual Spam Reviews Detection Based on Pre-Trained Word Embedding and Weighted Swarm Support Vector Machines. IEEE Access, , 11:72250-72271. Open version Published version
  11. Moreno-Torres, S., Mora-García, A.M., Carmona-Murillo, J., Galeano-Brajones, J. Advanced Ant Colony Optimization Algorithm for Service Function Chaining in Computer Networks. Submitted to IEEE Access, .
  12. Mora, A.M., Merino, P., Hernández, D., García-Sánchez, P., Fernández-Ares, A.J. Applying Evolutionary Methods for the Optimization of an Intrusion Detection System to Detect Anomalies in Network Traffic Flows. Applications of Nature-inspired Computing and Optimization Techniques. Advances in Computers Series, . Published version
  13. Wasielewska, K., Soukup, D., Čejka, T., Camacho, J. Evaluation of the Limit of Detection in Network Dataset Quality Assessment with PerQoDA.. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD, , 1753. Open version Published version

International Conference Papers

  1. Mora, A.M. , Victoria-Mohammed, J., Medina-Medina, N., Valenzuela-Valdés, J.F. Applying Evolutionary Algorithms for Service Function Chaining in 5G Networks. IEEE CEC 2024 (WCCI 2024), Yokohama (Japan), . Published version
  2. Mora, A.M., Fuentes-Izquiano, S. Combining Genetic Algorithms and Ant Colony Optimization for the Effective Allocation of Virtual Network Functions in a 5G Networ. EVO, Waels (UK), .
  3. Camacho, J. The complex interplay between Data and Model Quality. A case study in traffic anomaly detection. IRTF NMRG meeting 121, Dublin (Ireland), . Presentation Open version Published version
  4. Adán-López, R., Fernández-Martínez, D., Rodríguez-Gómez, R.A., Camacho, J. Coupled Design and Analysis of Experiments in Network Management. 37th IEEE/IFIP Network Operations and Management Symposium (NOMS 2024) , Seoul (Korea), . Poster Published version
  5. Camacho, J. ANOVA Simultaneous Component Analysis for the Efficient Exploration of Massive Network Traffic Data. 37th IEEE/IFIP Network Operations and Management Symposium (NOMS 2024) , Seoul (Korea), . Poster Published version
  6. Medina-Arco, J.G., Magán-Carrión, R., Rodríguez-Gómez, R.A. Exploring Hidden Anomalies in UGR’16 Network Dataset with Kitsune. Flexible Query Answering Systems (FQAS23), Springer Nature Switzerland, 2023, pp. 194-205, Mallorca (Spain), . Open version Published version
  7. Medina-Romero, J., Mora, A.M., Valenzuela-Valdés, J.F., Castillo, P.A. Applying Data Mining and Machine Learning Techniques to Predict Powerlifting Results. Engineering Proceedings 39 (1), 20, International conference on Time Series and Forecasting (ITISE 2023), Gran Canaria (Spain), . Open version
  8. Camacho, J. NetMob 2013 Data Analysis with ASCA. Netmob 2023, Madrid (Spain), . Presentation
  9. Camacho, J. Simulation Power Curves in ASCA. Topics in Chemometrics, Rostock (Germany), . Presentation
  10. Wasielewska, K. Network Dataset Quality Problem. IRTF NMRG meeting 116 (Online), . Open version
  11. Mora, A.M., Arenas, M.G., Romero-Horno, A., Castillo, P.A., Camacho, J. Optimizing an IDS (Intrusion Detection System) by means of Advanced Metaheuristics. International Work-Conference on Artificial Neural Networks (IWANN 2023), Ponta Delgada (Portugal), . Open version Published version
  12. Mora, A.M., Merino, P., Hernández, D. Enhancing an Intrusion Detection System by means of Evolutionary Approaches. International Conference on the Applications of Evolutionary Computation (EvoAPPS 2023), Brno ( Czech Republic), . Published version
  13. Camacho, J., Wasielewska, K., Espinosa, P., Fuentes-García, M. Quality In / Quality Out: Data quality more relevant than model choice in anomaly detection with the UGR'16. IEEE/IFIP Network Operation and Service Management (NOMS), Miami (USA), . Open version Published version
  14. Wasielewska, K., Soukup, D., Čejka, T., Camacho, J. Evaluation of Detection Limit in Network Dataset Quality Assessment with Permutation Testing. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2022, 4th Workshop on Machine Learning for Cybersecurity (MLCS), Grenoble (France), . Open version Published version
  15. Camacho, J., Wasielewska, K., Soukup, D., Čejka, T. Dataset Quality Assessment in Autonomous Networks with Permutation Testing. Seventh IEEE/IFIP International Workshop on Analytics for Network and Service Management, Bucarest (Hungary), . Open version Published version
  16. Wasielewska, K., Soukup, D., Čejka, T., Camacho, J. Dataset Quality Assessment in Autonomous Networks with PermutationTesting. The 10th Prague Embedded Systems Workshop, Horoměřice (Czech Republic ), .
  17. Álvarez-Terribas, F., Magán-Carrión, R., Maciá-Fernández, G., Mora García, A.M. A Deep Learning-Based Approach for Mimicking Network Topologies: The Neris Botnet as a Case of Study. International Joint Conference 15th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2022) and 13th International Conference on EUropean Transnational Education (ICEUTE 2022), . Published version
  18. Camacho, J. Networkmetrics for Network Monitoring and Security. Invited Talk. The 10th Prague Embedded Systems Workshop, Horoměřice (Czech Republic ), . Presentation
  19. Mañas-Martinez, E., Cabrera, E., Wasielewska, K., Camacho, J. Mining Social Interactions in Connection Traces of a Campus Wi-Fi Network. ACM Conference (SIGCOMM’21), New York (USA), . Poster Open version Published version
  20. Cuberos, F., Herrera, I., Wasielewska, K.,Camacho, J. Network Tomography and Partial Least Squares for Traffic Matrix Estimation. 17th International Conference on Network and Service Management (CNSM 2021), Izmir (Turkey), . Open version Published version

Deliverables

  1. Deliverable 1: Activity and Management Report – Half Term: Accepted as VERY SATISFYING Last modification: 22/05/2023
  2. Deliverable 2: Activity and Management Report – End of Project: Submitted Last modification: 25/11/2024
  3. Deliverable 3: Data Management Plan (Spanish) Last modification: 20/11/2024
  4. Deliverable 4: Quality Assurance & Risk Management Plan (Spanish) Last modification: 25/11/2024
  5. Deliverable 5: Requirement Analysis Report (Spanish) Last modification: 22/05/2023
  6. Deliverable 6: SDN Laboratory blueprint (Spanish) Last modification: 22/05/2023
  7. Deliverable 7: DAaaS blueprint (Spanish) Last modification: 22/05/2023
  8. Deliverable 8: SDN Laboratory Up and Running (ETSIIT 3.2) Last modification: 01/05/2023
  9. Deliverable 9: DAaaS Up and Running Last modification: 29/05/2023
  10. Deliverable 10: Web page Last modification: 25/11/2024
  11. Deliverable 11: The 1st IEEE/IFIP workshop on Quality of Data in Network Telemetry (QoDaNeT 2024) Last modification: 06/05/2023

Datasets

  1. UGR16 Feature data This repository contains the four feature data variants of UGR'16 used in the following papers:

    • Camacho, J., Wasielewska, K., Espinosa, P., Fuentes-García, M. Quality In / Quality Out: Data quality more relevant than model choice in anomaly detection with the UGR’16. IEEE/IFIP Network Operations and Management Symposium. Miami, USA. 2023.

    • Camacho, J., Rodríguez-Gómez, R.A. Data quality tools to optimize an anomaly detection benchmark. Submitted to Data, 2024.

    Please, make sure to reference the last paper when using the data, and also the original paper of UGR'16:

    • Maciá-Fernández, G., Camacho, J., Magán-Carrión, R., García-Teodoro, P., Therón, R. Ugr'16: a new dataset for the evaluation of cyclostationarity-based network IDSs. Computer & Security, 2018, 73: 411-424.

  2. Dartmouth Feature data This repository contains the feature data of Dartmouth dataset used in the following paper:

    • Camacho, J., Wasielewska, K., Bro R., Kotz, D. Interpretable Learning in Multivariate Big Data Analysis for Network Monitoring. IEEE Transactions of Network and Service Management, 2024, 21(3):2926-2943.

    Please, make sure to reference the paper when using the data, and also the original paper of the Dartmouth dataset:

    • Camacho, J., McDonald, C., Peterson, R., Zhou, X. Longitudinal Analysis of a Campus Wi-Fi Network. Computer Networks. 2020, 179, 107103.

Videos

  1. SDN Laboratory Software Defined Networks (SDN) research laboratory at the University of Granada.
  2. DAaaS Data Analysis as a Service (DAaaS) is an online service designed to facilitate the analysis and interpretation of traffic data. See more info.
  3. DAaaS Tutorial Part I Hands-on tutorial of the DAaaS
  4. DAaaS Tutorial Part II Hands-on tutorial of the DAaaS
  5. Data-Model Quality Interaction NMRG IETF 101 Presentation on data quality

Thesis

PhD Thesis
  • A. Al-Zoubi. Spam Reviews Detection Models in Multilingual Contexts applying Sentiment Analysis, Metaheuristics, and Advanced Word Embedding. Supervision: A.M. Mora, H. Faris (University of Jordan). Universidad de Granada. 2024.
MSc Thesis
  • Sergio Fernández Morales. Aprendizaje Automático de Características en Multivariate Big Data Analysis (MBDA) para el análisis de tráfico en red (in Spanish). Supervision: J. Camacho, R. Magán (UGR). Universidad de Granada. 2023.
  • Luis Vargas Maldonado. Tools for anti-tracking. Study of web breakages caused by ad-blockers. Supervision: J. Camacho, C. Troncoso (EPFL). Universidad de Granada. 2022.
  • Pablo Estévez González. Anomaly Detection with Machine Learning Tools. Supervision: J. Camacho, J. Suárez-Varela (UPC). Universidad de Granada. 2022.
  • José Gabriel Marín Martín. Hijacking en BGP. Supervision: J. Camacho. Universidad de Granada. 2021.
  • Elena Cabrera Garrido. Privacy Evaluation in the Analysis of Wi-Fi Connection Traces. Supervision: J. Camacho. Universidad de Granada. 2021.
  • Manuel Jurado Vázquez. Parsing in Cybersec. Supervision: J. Camacho. Universidad de Granada. 2021.
BSc Thesis
  • Aitor Jiménez Segura. ILP Optimization in SDN Networks. Supervision: J. Camacho. Universidad de Granada. 2024.
  • Antonio Aguilera González. Testbed for service placement optimization in the continuum Cloud-Fog-Edge. Supervision: J. Camacho. Universidad de Granada. 2024.
  • Mario Guisado García. Aplicación de Metaheurísticas Cuánticas para la Optimización de Cadenas de Servicios en un Modelo de Red 5G. Supervision: A.M. Mora, A. Borrallo (Fujitsu). Universidad de Granada. 2024.
  • Daniel Monjas Miguélez. Tomografía de Red basada en Aproximaciones Mínimo-Cuadráticas Alternas (in Spanish). Supervision: J. Camacho. Universidad de Granada. 2023.
  • José Miguel González Cañadas. Detección de Anomalías en Redes con Aprendizaje Automático y Matrices de Covarianza (in Spanish). Supervision: J. Camacho. Universidad de Granada. 2023.
  • Marta Eugenia Gavilán Sierra. Evaluando la calidad de benchmarks para la detección de anomalías en red (in Spanish). Supervision: J. Camacho. Universidad de Granada. 2023.
  • Renan Barreto Farias. Laboratorio de emulación SDN (in Spanish). Supervision: J. Camacho. Universidad de Granada. 2023.
  • Alvaro García Rodríguez. Uso de generadores de tráfico para la captura de tráfico en redes SDN. Supervision: Rafael A. Rodríguez-Gómez. Universidad de Granada. 2023.
  • Felipe González López. Aplicación de técnicas avanzadas de aprendizaje automático para la detección de eventos de seguridad en redes de comunicaciones. Supervision: A.M. Mora, R. Magán (UGR). Universidad de Granada. 2023.
  • Francisco J. Gallardo Molina. Diseño y estudio de una Red Definida por Software (SDN) adaptativa usando software de simulación. Supervision: A.M. Mora, J.F. Valenzuela (UGR). Universidad de Granada. 2023.
  • Andrea Pérez Jáimez. Multivariate Big Data Analysis of Routing Data. Supervision: J. Camacho. Universidad de Granada. 2022.
  • Andrés Romero Horno. Optimization of an IDS (Intrusion Detection System) using advanced metaheuristics. Supervision: A. Mora, J.F. Valenzuela (UGR). Universidad de Granada. 2022.
  • Javier Victoria Mohammed. Application of Evolutionary Algorithms for service chain optimization in a 5G network model. Supervision: A. Mora, J.F. Valenzuela (UGR). Universidad de Granada. 2022.
  • Enrique Julio Castellano Martín. Emulation laboratory for SDN networks. Supervision: J. Camacho, R. Rodríguez-Gómez. Universidad de Granada. 2022.
  • Federico Moles Sánchez. Detection of Cybersecurity Incidents through InterpretableMachine Learning with Zeek. Supervision: J. Camacho. Universidad de Granada. 2021.