Network intrusion clustering based on fuzzy C-Means and modified Kohonen neural network

Kohonen neural network recognizes and clarifies substantive network data, but with a long running time and a slow convergence process. To solve this problem, a network intrusion clustering method is presented in this paper. Specifically, the training data is pretreated using Fuzzy C-Means (FCM). Then some selected data will be trained with using Kohonen neural network. Meanwhile, to speed up the convergence process of Kohonen neural network and to form a better optimized network topology, a neighbourhood function is established for the competing neuron. Each neuron has neighbourhood topology collections. The data simulation results demonstrate the efficiency and effectiveness of the proposed algorithm.