Abstract
Industry 4.0, also known as the Fourth Industrial Revolution, is
characterized by the incorporation of advanced manufacturing
technologies such as the Internet of Things (IoT), Artificial
Intelligence (AI), and automation. With the increasing adoption of
Industry 4.0 technologies, it becomes crucial to implement effective
security measures to safeguard these systems from cyber attacks. The
development of intrusion detection systems (IDS) that can detect and
respond to cyber threats in real-time is crucial for securing Industry
4.0 systems. This research topic seeks to investigate the various
techniques and methodologies employed in developing IDS for Industry 4.0
systems, with a particular concentration on identifying the most
effective solutions for protecting these systems from cyber attacks. In
this study, we compared supervised and unsupervised intrusion detection
algorithms. We utilized data collected from heterogeneous sources,
including Telemetry datasets of IoT and The industrial Internet of
things (IIoT) sensors, Operating systems (OS) datasets of Windows 7 and
10, as well as Ubuntu 14 and 18 TLS and Network traffic datasets
simulated by the School of Engineering and Information Technology
(SEIT), UNSW Canberra @ the Australian Defence Force Academy (ADFA). The
preliminary results of IDS accuracy are extremely encouraging on the
selected data for this study (Windows OS and Ubuntu OS), which motivates
the continuance of this line of inquiry using a variety of other data
sources to formulate a general recommendation of IDS for Industry 4.0.