Anomaly traffic detection based on PCA and SFAM

Abstract EN

Intrusion Detection System (IDS) has been an important tool for network security.

However, existing IDSs that have been proposed do not perform well for anomaly traffics especially Remote to Local (R2L) attack which is one of the most concerns.

We thus propose a new efficient technique to improve IDS performance focusing mainly on R2L attacks.

The Principal Component Analysis (PCA) and Simplified Fuzzy Adaptive resonance theory Map (SFAM) are used to work collaboratively to perform feature selection.

The results of our experiment based on KDD Cup’99 dataset show that this hybrid method improves classification performance of R2L attack significantly comparing to other techniques while classification of the other types of attacks are still well performing.