@misc{10481/55289,
year = {2019},
month = {1},
url = {http://hdl.handle.net/10481/55289},
abstract = {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.},
keywords = {Multivariate Statistical Network Monitoring},
keywords = {Anomaly Detection},
keywords = {Intrusion Detection},
keywords = {Semi-supervised learning},
keywords = {Partial Least Squares regression},
keywords = {Principal components analysis},
title = {Semi-supervised Multivariate Statistical Network Monitoring for Learning Security Threats},
doi = {10.1109/TIFS.2019.2894358},
author = {Camacho Páez, José and Macía Fernández, Gabriel and Fuentes García, Noemí Marta and Saccenti, Edoardo},
}