NESG

Icono Icono

Icono Icono

Semi-supervised Multivariate Statistical Network Monitoring for Learning Security Threats

José Camacho; Gabriel Maciá-Fernández; Noemí Marta Fuentes-García; Edoardo Saccenti
Abstract:
This paper presents a semi-supervised approach for intrusion detection. The method extends the unsupervised multivariate statistical network monitoring approach based on the 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, capable 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.
Research areas:
Year:
2019
Type of Publication:
Article
Keywords:
Principal component analysis; Optimization; Anomaly detection; Monitoring; Intrusion detection; Machine learning
Journal:
IEEE Transactions on Information Forensics and Security
Volume:
14
Number:
8
Pages:
2179 - 2189
Month:
Aug.
DOI:
10.1109/TIFS.2019.2894358
Hits: 220