Machine Learning-Based Detection of Anomalies, Intrusions and Threats in Industrial Control Systems

Abstract

Industrial Control Systems are a part of the
infrastructure we use today. They are used in power
grids, manufacturing systems, water treatment facilities
and transportation networks. As we use digital technologies
and connect things to the internet, these systems are more
at risk of being hacked. This means that Industrial Control
Systems are vulnerable to cyber threats like people getting in
without permission, malware attacks and system intrusions.
The old ways of keeping them safe, like using signature-based
detection and rule-based monitoring, are not good enough
to stop complicated and changing cyber attacks. So we need
security solutions that can adapt and learn to protect Industrial
Control Systems.
This paper is about using machine learning to detect
anomalies, intrusions and threats in Industrial Control Systems.
The idea is to use data-driven techniques to look at network
traffic, system logs and sensor data from Industrial Control
Systems. By using machine learning algorithms like Decision
Trees, Random Forest, Support Vector Machines and Neural
Networks, the system can learn what normal operations look
like and find things that do not seem right. This can help us
find activities.
To do this, we need to collect data from networks, clean
it up to remove mistakes and find the important parts like
communication patterns, command sequences and system
behaviours. Then we train models to sort system activities into
abnormal groups. This helps us find cyber threats early. We
also use anomaly detection to find attacks that we have not seen
before.
We tested this approach. It works really well. It can detect
threats with accuracy, precision and recall and it can find both
known and unknown threats without giving too many false
alarms. The system can also monitor things in time so we can
respond quickly to potential security incidents.
Using machine learning in Industrial Control Systems
security makes it better at detecting cyber threats. It also makes
the systems more resilient. Ensures that critical industrial
infrastructure works safely and reliably. In the future, we might
work on making the models bigger using learning and creating
adaptive defence mechanisms for industrial environments that
...
change. Industrial Control Systems will be safer with these
security solutions.