Host anomaly detection performance analysis based on system call of neuro-fuzzy using Soundex algorithm and N-gram technique

Abstract

To improve the anomaly intrusion detection system using system calls, this study focuses on neuro-fuzzy learning using the Soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern. That is, by changing variable length sequential system call data into a fixed length behavior pattern using the Soundex algorithm, this study conducted backpropagation neural networks with fuzzy membership function. The neuro-fuzzy and N-gram techniques are applied for anomaly intrusion detection of system calls using sendmail data of UNM to demonstrate its performance.