Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set-Reference-Cited by-同舟云学术

Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set

Published:2018-08-27 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 13th International Conference on Availability, Reliability and Security
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Author:

Anton Simon Duque¹,Kanoor Suneetha¹,Fraunholz Daniel¹,Schotten Hans Dieter¹

Affiliation:

1. Intelligent Networks Research Group, German Research Center for Artificial Intelligence, Kaiserslautern, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3230833.3232818

Reference52 articles.

1. 2018. Package rpart. 2018. Package rpart.

2. Khaled Alsabti Sanjay Ranka and Vineet Singh. 1997. An efficient k-means clustering algorithm. Electrical Engineering and Computer Science (January 1997). Khaled Alsabti Sanjay Ranka and Vineet Singh. 1997. An efficient k-means clustering algorithm. Electrical Engineering and Computer Science (January 1997).

3. An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression;Altman N. S.;The American Statistician,1992

4. Network Anomaly Detection: Methods, Systems and Tools

5. Human probability matching behaviour in response to alarms of varying reliability

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