What is it about?
This work explains the challenges we have faced in adopting Machine Learning Algorithms for intrusion detection in Industrial Control Systems (ICS). This study is insightful with a summary of several projects both using examples from design and data-centric techniques. Each study is concluding with a lesson learned and some recommendations are made for future research.
Featured Image
Photo by Arseny Togulev on Unsplash
Why is it important?
We are witnessing a huge increase in the use of machine learning algorithms to solve problems in multiple domains. Cybersecurity is no exception, machine learning methods have been used from attacks to defense across the board. With the availability of data from ICS testbeds, machine learning is becoming ever more prevalent in critical systems research including but not limited to anomaly detection. Based on extensive research projects utilizing state of the art testbeds at Singapore University of Technology and Design, this article summarises first-hand challenges faced, lessons learned, and recommendations for future work.
Perspectives
I am excited to work on this project with Gauthama and Prof. Aditya Mathur. What started as a brainstorming session over dinner turned out to be exciting work and winning us the best paper award as well. The exciting part is recommendations made for future research and we are onto it already, hope to share more interesting results soon.
Chuadhry Mujeeb Ahmed
Singapore University of Technology and Design
Read the Original
This page is a summary of: Challenges in Machine Learning based approaches for Real-Time Anomaly Detection in Industrial Control Systems, October 2020, ACM (Association for Computing Machinery),
DOI: 10.1145/3384941.3409588.
You can read the full text:
Resources
Contributors
The following have contributed to this page
