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:

• Data processing and networkmetrics

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