Enhanced SCADA IDS Security by Using MSOM Hybrid Unsupervised Algorithm

In Self-Organizing Maps (SOM) are unsupervised neural networks that cluster high dimensional data and transform complex inputs into easily understandable inputs. To find the closest distance weight factor, it maps input space to low space. The Closest node point is denoted as a neuron. It classifies based on these neurons. reduction of dimensionality grid clustering using neurons makes observe similarities between data. our proposed Mutated Self Organizing (MSOM) approach, we have two intentions. One eliminate learning rate decrease neighborhood size next one out outliers in network. first by calculating median (MD) each with its neighbor nodes. Then those values compared another. case, if any MD significantly varies from rest then declared anomaly second phase, quantization error (QE) instance center.