Intruder Detection Using Fuzzy Min-Max Neural Network and A Principal Component Analysis (PCA) in Network Data - Aldubai, A F; Humbe, V H; Chowhan, S S; Aldubai, Y F (International Journal of Computer Sciences and Engineering, 2025)
To improve classification performance and prediction time of (IDS) system when a few number of large hyperboxes are formed in the network. We have proposed FMM NN and PCA features extraction algorithms. The process of a system consisting of the pre-processing dataset based on interquartile ranges filter and features extraction. Followed by the Fuzzy min-max neural network (FMM NN), which define as supervised learning classifier, that utilizes a fuzzy set hyberbox as pattern classes for learning and classification. Each hyberbox consists of min and mix points of opposite corners of hyberbox with corresponding to membership function. Two real-time and faster streaming datasets (KDD99 and NSL-KDD) are used to empirically evaluate the effectiveness of the proposed FMM NN system. The results are analyzed and compared with others existing systems and published results.