Enhanced Fuzzy Clustering with Generalized Neural Network Regression for Intrusion Data Classification

While fuzzy clustering enables data clustering analysis, its effectiveness on network intrusion data is limited by high-dimensional features and minimal inter-class differences, leading to inaccurate classification of many intrusion patterns. This case study implements a hybrid clustering algorithm combining fuzzy clustering with generalized neural network regression to achieve more precise intrusion data classification, with algorithmic implementations focusing on feature space optimization and probabilistic assignment mechanisms.