| Title | Systematic and Efficient Anomaly Detection Framework using Machine Learning on Public ICS Datasets |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Baptiste, Millot, Julien, Francq, Franck, Sicard |
| Conference Name | 2021 IEEE International Conference on Cyber Security and Resilience (CSR) |
| Keywords | anomaly detection, Classification algorithms, cybersecurity, Data preprocessing, ICS Anomaly Detection, industrial control systems, machine learning, machine learning algorithms, pubcrawl, resilience, Resiliency, Scalability, security, Systematics, Transportation, Writing |
| Abstract | Industrial Control Systems (ICSs) are used in several domains such as Transportation, Manufacturing, Defense and Power Generation and Distribution. ICSs deal with complex physical systems in order to achieve an industrial purpose with operational safety. Security has not been taken into account by design in these systems that makes them vulnerable to cyberattacks.In this paper, we rely on existing public ICS datasets as well as on the existing literature of Machine Learning (ML) applications for anomaly detection in ICSs in order to improve detection scores. To perform this purpose, we propose a systematic framework, relying on established ML algorithms and suitable data preprocessing methods, which allows us to quickly get efficient, and surprisingly, better results than the literature. Finally, some recommendations for future public ICS dataset generations end this paper, which would be fruitful for improving future attack detection models and then protect new ICSs designed in the next future. |
| DOI | 10.1109/CSR51186.2021.9527911 |
| Citation Key | baptiste_systematic_2021 |
Systematic and Efficient Anomaly Detection Framework using Machine Learning on Public ICS Datasets