Improving the Intrusion Detection System for NSL-KDD Dataset based on PCA-Fuzzy Clustering-KNN

Nowadays, information security is extremely critical issues for every organization to protect information from the useless data on the manipulation of network traffic or intrusion. Intrusion detection system has one of the important roles to prevent data or information from malicious behaviors because its capable of detecting attacks in several available environments. Thereafter, many researches concentrate on developing new algorithms to treat the Dataset by different way. In this work, we suggest a new proposed PCA-fuzzy Clustering-KNN method that means ensemble of Analysis of Principal Component and Fuzzy Clustering with K-Nearest Neighbor feature selection technics. However, we perform two main class classifications to construct our suggested model. Then, to check the robustness of model we used as well-known Dataset NSL-KDD used for analysis of anomaly. This Dataset is based on benchmark data used for intrusion detection, KDDCup 1999. Therefore, we analyse NSL-KDD Dataset using PCA-fuzzy Clustering-KNN analytic and try to define the performance of incident using machine learning algorithms, the algorithm learns what type of attacks are found in which classes in order to improve the classification accuracy and reduce high false alarm rate and detects the maximum of detection rate from Dataset as shown by the numerical results.