Semi-supervised Multivariate Statistical Network Monitoring for Learning Security Threats Camacho Páez, José Macía Fernández, Gabriel Fuentes García, Noemí Marta Saccenti, Edoardo Multivariate Statistical Network Monitoring Anomaly Detection Intrusion Detection Semi-supervised learning Partial Least Squares regression Principal components analysis This paper presents a semi-supervised approach for intrusion detection. The method extends the unsupervised Multivariate Statistical Network Monitoring approach based on Principal Component Analysis by introducing a supervised optimization technique to learn the optimum scaling in the input data. It inherits the advantages of the unsupervised strategy, capable of uncovering new threats, with that of supervised strategies, able of learning the pattern of a targeted threat. The supervised learning is based on an extension of the gradient descent method based on Partial Least Squares (PLS). Moreover, we enhance this method by using sparse PLS variants. The practical application of the system is demonstrated on a recently published real case study, showing relevant improvements in detection performance and in the interpretation of the attacks. 2019-04-01T06:28:13Z 2019-04-01T06:28:13Z 2019-01 info:eu-repo/semantics/article J. Camacho, G. Maciá-Fernández, N. M. Fuentes-García and E. Saccenti, "Semi-supervised Multivariate Statistical Network Monitoring for Learning Security Threats," in IEEE Transactions on Information Forensics and Security. doi: 10.1109/TIFS.2019.2894358 http://hdl.handle.net/10481/55289 10.1109/TIFS.2019.2894358 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial-SinDerivadas 3.0 España