Enhanced SCADA IDS Security by Using MSOM Hybrid Unsupervised Algorithm

Sangeetha K., Shitharth S., Gouse Baig Mohammed

Source Title: International Journal of Web-Based Learning and Teaching Technologies (IJWLTT)17(2)

ISSN: 1548-1093|EISSN: 1548-1107|EISBN13: 9781799893516|DOI: 10.4018/IJWLTT.20220301.oa2

MLA

Sangeetha K., et al. "Enhanced SCADA IDS Security by Using MSOM Hybrid Unsupervised Algorithm." IJWLTT vol.17, no.2 2022: pp.1-9. http://doi.org/10.4018/IJWLTT.20220301.oa2

APA

Sangeetha K., Shitharth S., & Mohammed, G. B. (2022). Enhanced SCADA IDS Security by Using MSOM Hybrid Unsupervised Algorithm. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 17(2), 1-9. http://doi.org/10.4018/IJWLTT.20220301.oa2

Chicago

Sangeetha K., Shitharth S., and Gouse Baig Mohammed. "Enhanced SCADA IDS Security by Using MSOM Hybrid Unsupervised Algorithm," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT) 17, no.2: 1-9. http://doi.org/10.4018/IJWLTT.20220301.oa2

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Abstract

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 data. In our proposed Mutated Self Organizing Maps (MSOM) approach, we have two intentions. One is to eliminate the learning rate and to decrease the neighborhood size and the next one is to find out the outliers in the network. The first one is by calculating the median distance (MD) between each node with its neighbor nodes. Then those median values are compared with one another. In case, if any of the MD values significantly varies from the rest then it is declared as anomaly nodes. In the second phase, we find out the quantization error (QE) in each instance from the cluster center.