Fuzzy Controlled Network Intrusion Detection System (FC-NIDS)
Neeraj Kumar1, Upendra Kumar2
1Neeraj Kumar*, Research Scholar, Computer Science & Engineering, Birla Institute of Technology, Mesra, India.
2Dr. Upendra Kumar, Department of Computer Science & Engineering, Birla Institute of Technology, Mesra, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on 24 November, 2019. | Manuscript published on December 10, 2019. | PP: 228-235 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6134129219/2019©BEIESP | DOI: 10.35940/ijitee.B6134.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Intrusion Detection System (IDS) is the nearly all imperative constituent of computer network security. IDSs are designed to comprehend intrusion attempts in incoming network traffic shrewdly. It deals with big volume of data containing immaterial and outmoded features, which lead to delay in training as well as testing procedures. Therefore, to minimize the false alarm and computation complexity, the features selection technique for intrusion detection has been implemented. In this paper PCA (Principal Component Analysis) and Fuzzy Inference System (FIS) have been used on kdd99 dataset to develop FC-NIDS model. PCA is used to select the attacked features to minimize the computational work, while FIS is used to develop a fuzzy inference system for accuracy in prophecy using MATLAB. The results of the experiment are tested on UCI data sets as a standard bench-mark. It has been found efficient for true prediction of intrusion as well as to reduce the false alarm rate. The proposed fuzzy logic controller IDS (FC-NIDS), is passable to covenant with signature and anomaly based attacks to get enhanced intrusion detection, decreases false alarm and to optimize complexity.
Keywords: Intrusion, Confusion Matrix, FDR, FLC, IDS, KDD, PCA, Precision, Recall, SVM.
Scope of the Article: Fuzzy Logics