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ThinkMind // CYBER 2021, The Sixth International Conference on Cyber-Technologies and Cyber-Systems // View article cyber_2021_1_260_80114


An Automated Reverse Engineering Cyber Module for 5G/B5G/6G: ML-Facilitated Pre-“ret” Discernment Module for Industrial Process Programmable Logic Controllers

Authors:
Steve Chan

Keywords: cybersecurity; industrial control system; programmable logic controller; Industry 4.0; Industrial Internet of Things; smart manufacturing; smart grid; 5G; machine learning; artificial intelligence; automated reverse engineering.

Abstract:
Industrial Control System (ICS) components have been subject to heightened cyber risk as hardware/software supply chain vulnerabilities have been illuminated and cyberattacks have become increasingly sophisticated. At the center of this ICS cyber maelstrom is the Programmable Logic Controller (PLC), a key component of Industry 4.0, as it is a main controller for physical processes (e.g., the control of an actuator). Many PLCs were designed for another era; they are resource-constrained, non-optimized, and beset with a variety of legacy facets (e.g., compiler, programming language, etc). This described sub-optimal paradigm also exists within the rubric of standards that specify the time interval between signal ingestion and actuation (e.g., IEEE 1547 specifies 2 seconds) for the operating environment. Hence, the designing/architecting/implementing of a light computational footprint continuous Monitoring/Detecting/Mitigating Module (MDMM) is non-trivial. This paper investigates a specific scenario of an ICS PLC operating within a 5G Ultra-Reliable Low-Latency Communications (URLLC) inter-PLC context and posits a viable MDMM construct that can operate within the paradigm. Central to its viability, the MDMM leverages apriori scan cycle traffic, utilizes Machine Learning (ML)-facilitated PLC logic/code optimization, and endeavors to undertake mitigation via a bespoke Automated Reverse Engineering (ARE) mechanism. The introduced MDMM requires further quantitative benchmarking, but the initial experimental results show promise.

Pages: 93 to 100

Copyright: Copyright (c) IARIA, 2021

Publication date: October 3, 2021

Published in: conference

ISSN: 2519-8599

ISBN: 978-1-61208-893-8

Location: Barcelona, Spain

Dates: from October 3, 2021 to October 7, 2021

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