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Enhanced SCADA IDS Security by Using MSOM Hybrid Unsupervised Algorithm
2022
International Journal of Web-Based Learning and Teaching Technologies
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 and weight factor, it maps high dimensional input space to low dimensional input space. The Closest node to data point is denoted as a neuron. It classifies the input data based on these neurons. The reduction of dimensionality and grid clustering using neurons makes to observe similarities between the
doi:10.4018/ijwltt.20220301.oa2
fatcat:2tze2luiqrerfdqxszpwcuywoi