Design and Development of Suginer Filter for Intrusion Detection Using Real Time Network Data

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 Intrusion-Detection System (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 Neurofuzzy 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