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Data processing and networkmetrics



The networkmetrics research line seeks to take advantage of multivariate analysis and machine learning tools to tackle problems in communication networks, with cybersecurity as main example. An effective detection of cybersecurity incidents requires the combination of several and disparate data sources. This makes cybersec a typical Big Data problem, where the challenge is to handle tons of information from heterogeneous sources at a fastpace. In NESG, we develop new analysis methods to handle Multivariate Big Data, which are also of value in applications like IoT monitoring or Industry 4.0, and in other domains, like chemometrics, bioinformatics and personalized medicine.



  • Camacho, J., Bro, R. & Kotz, D. (2020). MBDA in Action. [More] 
  • Camacho, J., Smilde, A. K., Sacc & Westerhuis, J. (2020). All Sparse PCS Models Are Wrong, But Some Are Useful. Part I. Computation Scores, Residuals and Explained Variance. Chemometrics and Intelligent Laboratory Systems, 196, 1039072-. [More] 
  • Magán-Carrión, R., Camacho, J., Maciá-Fernández, G. & Ruiz-Zafra, A. (2020). MSNM-Sensor: An Effective Tool for Real-Time Monitoring and Anomaly Detection in Complex Networks and Systems. International Journal of Distributed Sensor Networks, . [More] 
  • Camacho, J., Acar, J. & Rasmunssen, E. (2020). Cross-product Penalized Component Analysis (X-CAN). Chemometrics and Intelligent Laboratory Systems, . [More] 
  • Camacho, J., Acar, E., Rasmussen, M. & Bro, R. (2019). X-CAN: Cross-Penalized Component Analysis. Scandinavian Symposium on Chemometrics (SSC16), . [More]