A novel method to detect adversaries using MSOM algorithm’s longitudinal conjecture model in SCADA network

Abstract

-In a neural network, for classifying any high dimensional data, SOM (Self OrganizingMaps) is highly preferred. Self-Organizing Maps (SOM) are unsupervised neural networks thatcluster high dimensional data and transform complex inputs into easily understandableinputs. It works on data sets and used to find previously unknown patterns. Patterns help incategorizing elements for finding an association between them. They can detect theadversaries and defects in the data. Neural networks are considered as one of the prolificunsupervised learning methods which are a fast, and powerful technique that can be used tosolve many real-world problems. These networks are widely used for data representation. Anunsupervised algorithm has to understand the patterns in the data and then further process thedesired output. This research proposes a modified neural network algorithm called MUSOM(mutated Self Organizing Maps). This algorithm has two major conjectures based on attacker’sresponse on incoming packets from SCADA sensor. It has two components 1.nodes and2.neurons. These nodes and neurons are inter connected with each other. One is used toaccomplish fault tolerance, error correction and dimensionality reduction concerning theunknown anomalies. The next one is to find the outlying anomalies in the network

Shitharth Selvarajan

Lecturer in Cyber Security

My research interests include Cyber Security, Blockchain, Critical Infrastructure & Systems, Network Security & Ethical Hacking.