preprints.org > engineering > electrical and electronic engineering > doi: 10.20944/preprints202307.0747.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 7 July 2023 / Approved: 11 July 2023 / Online: 12 July 2023 (09:22:09 CEST)

In recent times, the process control network (PCN) of oil and gas installation has been subjected to amorphous cyber-attacks which include Denial-of-Service (DoS), Distributed-Denial-of-Service (DDoS), Man-in-the-Middle (MitM) attacks, and this may have been caused majorly by the integration of open network to Operation Technology (OT) as a result of low-cost network expansion. The connection of the OT to the internet for firmware updates, third-party support, or vendor interventions, has exposed the industry to attacks. The inability to detect these unpredictable cyber-attacks exposes the PCN and a successful attack can lead to devastating effects. This paper reviews the different forms of cyber-attacks in PCN of oil and gas installations and proposes the use of machine learning algorithms to monitor data exchanges between the sensors, controllers, processes, and the final control elements on the network so as to detect anomalies in such data exchanges. Python 3.0 Libraries, Deep-Learning Toolkit, MATLAB, and Allen Bradley RSLogic 5000 PLC Emulator software were used in the simulation of process control. The outcome of the experiments shows the reliability and functionality of the different machine-learning algorithms in detecting these anomalies with significant precise attack detections identified using a coarse tree algorithm.

Amorphous Cyber-attacks; Process Control Network; Anomaly Detection; Machine Learning; Man-in-the-Middle Attacks; SCADA

Engineering, Electrical and Electronic Engineering

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