University of Leicester
Browse
Cyber_Security_2020_V1_3_Revise_16_04_2020__For_Joe_and_the_team_ (7).pdf (864.4 kB)

Evaluation of Machine Learning Algorithms for Anomaly Detection

Download (864.4 kB)
conference contribution
posted on 2020-05-21, 14:51 authored by N Elmrabit, F Zhou, F Li, Huiyu Zhou
The cyber-physical security of Industrial Control Systems (ICSs) represents an actual and worthwhile research topic. In this paper, we compare and evaluate different Machine Learning (ML) algorithms for anomaly detection in industrial control networks. We analyze supervised and unsupervised ML-based anomaly detection approaches using datasets extracted from the Secure Water Treatment (SWaT), a testbed developed to emulate a scaled-down real industrial plant. Our experiments show strengths and limitations of the two ML-based anomaly detection approaches for industrial networks.

Funding

This work was funded by EU Horizon 2020 DOMINOES Project (GrantNumber: 771066)

History

Citation

2019 IEEE International Symposium on Measurements & Networking (M&N)

Source

2019 IEEE International Symposium on Measurements & Networking (M&N)

Published in

2019 IEEE International Symposium on Measurements & Networking (M&N)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

2639-5061

isbn

978-1-7281-1273-2

Acceptance date

2019-05-16

Copyright date

2019

Spatial coverage

Catania, Italy

Temporal coverage: start date

2019-07-08

Temporal coverage: end date

2019-07-10

Language

en

Usage metrics

    University of Leicester Publications

    Categories

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC