Anomaly intrusion detection for system call using the soundex algorithm and neural networks

Abstract

To improve the anomaly intrusion detection system using system calls, this study focuses on supervisor learning neural networks 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 neural learning by using a backpropagation algorithm. The proposed method and N-gram technique are applied for anomaly intrusion detection of system call using sendmail data of UNM to demonstrate its performance.