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

In the original text, we discussed limitations of fuzzy clustering algorithms when analyzing network intrusion data. The primary challenges stem from high-dimensional feature spaces and subtle differences between intrusion categories, resulting in inaccurate classification of numerous attack patterns. To address this, our proposed solution implements a hybrid approach integrating fuzzy clustering with generalized neural network regression. This combined algorithm enhances classification accuracy through two key computational mechanisms: first, the fuzzy clustering component handles probabilistic data assignments using membership functions (typically implemented via fuzzy c-means with Euclidean distance metrics), while the generalized neural network regression module processes nonlinear feature relationships through multi-layer perceptrons with adaptive activation functions. The implementation typically involves preprocessing high-dimensional data using dimensionality reduction techniques like PCA before applying the hybrid classification model. This integrated approach significantly improves intrusion detection performance by enabling soft classification boundaries and handling complex feature interactions that traditional methods cannot capture effectively.