Comparative Sensory Data Monitoring Model Based On Multiple Algorithms between Server and Client PI within A Smart Manufacturing Setup

Tshepo Kukuni; Elisha Markus; Ben Kotze; Adnan M. Abu-Mahfouz

Author(s)

Tshepo Kukuni Department of Electrical, Electronic and Computer Engineering, Central University of Technology, Free-State, South-Africa

Elisha Markus Department of Electrical, Electronic and Computer Engineering, Central University of Technology, Free-State, South-Africa

Ben Kotze Department of Electrical, Electronic and Computer Engineering, Central University of Technology, Free-State, South-Africa

Adnan M. Abu-Mahfouz Council for Scientific and Industrial Research, Gauteng, Pretoria, South-Africa

Abstract

The optimal use of data in decision-making for instituting effective and efficient processes within the manufacturing sector is increasing rapidly. As a result, this digital transition poses high risk of cyber - attacks for various reasons such as financial gain etc. This research paper therefore aims at investigation the feasibility of a modelled system with the ability to correlate data between simulation model and physical model and the ability of such a model to cipher and decipher data without any losses. The presentation of such a model seeks to answer the research question looking at the impact of the encryption speed and its contributing to the data security quality and its influence in the implementation of security measures within a Smart Manufacturing Plant. The model setup was developed by creating two identical models based on the two PI4s and the application of the investigated algorithms on both PI4s with the same secret key that is used for both encryption (server-side) and decryption (client-side). Furthermore, the model setup was developed by implementing the shift rows and the mix column and inverse mix column on the 16X16 array based on the 128-bit-length. The results demonstrate that the developed model is secure and accurate without any loss of data. Furthermore, DES, Salsa29, RSA and DSA were tested and compared against each other utilising the same data file comprising of sensory data and the results demonstrate that all the five algorithms can cipher and decipher data without experiencing any data losses. However, the RSA and DSA execution times were 17ms and 21ms respectively, while the other AES executed at 4ns, DES at 3ns and Salsa29 2ns respectively. Therefore, this paper concludes that the investigated algorithms does provide high-level data-security, however, it is empirical to further investigate the optimization of RSA and DSA algorithms to ensure efficiency

Publication Details

Page(s) 408-419

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: S1, Year: 2024

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Keywords

machine learning, deep learning, Cyber-security, Internet of Things, intrusion detection, sensory data., smart manufacturing plant

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