Design and development of suginer filter for intrusion detection using real time network data

Abstract EN

By rapid use of the Internet and computer network all over the world makes security a major issues, so using the intrusion-detection system has become more important.

All the same, the primary issues of IDS are generating high false alarm rate and fails to detect attacks, which make system security more vulnerable.

This paper proposed a new concept of using Suginer Filter to identify IDS.

The Takagi-Sugeno fuzzy model is structured based on Neuro-fuzzy method to generate fuzzy rules and wiener filter is used to filter out attack as a noise signal using fuzzy rule generation.

These two methods are combined to detect intrusive behavior of the system.

The proposed suginer filter (Sugeno+Wiener) uses completely a different research structure to identify attacks and the experiment was evaluated on live network data collected, which shows that the proposed system achieves approximately 98.46% of accuracy and reduce false alarm rate to 0.08% in detecting different real time attacks.

From the obtained result it’s clear that the proposed system performs better when compared with other existing machine learning techniques