Ensemble Machine Learning for Intrusion Detection in Cyber-Physical Systems

Ensemble Machine Learning for Intrusion Detection in Cyber-Physical Systems

In this work, we evaluate the benefits of applying ensemble machine learning techniques to CPS attack detection, together with the application of data imbalance techniques. We also compare the performance improvements obtained from bagging, boosting, and stacking ensemble techniques. The stacking mo...

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Bibliographic Details
Published in:IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) pp. 1 - 2
Main Authors: Li, Hongwei, Chasaki, Danai
Format: Conference Proceeding
Language:English
Published: IEEE 10-05-2021
Subjects:
Computational modeling
Computer hacking
Conferences
Cyber-physical systems
Intrusion detection
Production
Stacking
Online Access:Get full text
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Summary:In this work, we evaluate the benefits of applying ensemble machine learning techniques to CPS attack detection, together with the application of data imbalance techniques. We also compare the performance improvements obtained from bagging, boosting, and stacking ensemble techniques. The stacking models that build upon bagging and boosting provide the best detection performance. After scoring both superior detection performance and low computation cost, the "Stack-2" models provide the best detection efficacy and can easily be deployed to production environment and can be scaled for the protection of hundreds of thousands of network flows per second.
DOI:10.1109/INFOCOMWKSHPS51825.2021.9484479