https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Evaluating Machine Learning Approaches for Cyber and Physical Anomalies in SCADA Systems
University Campus Bio-Medico di Roma, Unit of Automatic Control, Rome, Italy.
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation. University of Applied Sciences and Arts of Southern Switzerland, IDSIA USI-SUPSI, Department of Innovative Technologies, Lugano, Switzerland.ORCID iD: 0000-0002-2833-7196
University Campus Bio-Medico di Roma, Unit of Automatic Control, Rome, Italy.
University Campus Bio-Medico di Roma, Unit of Automatic Control, Rome, Italy.
2023 (English)In: Proc. IEEE Int. Conf. Cyber Security Resilience, CSR, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 412-417Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, machine learning (ML) techniques have been widely adopted as anomaly-based Intrusion Detection System in order to evaluate cyber and physical attacks against Industrial Control Systems. Nevertheless, a performance comparison of such techniques applied to multiple Cyber-Physical Systems datasets is still missing. In light of this, we propose a comparative study about the performance of four supervised ML-algorithms, Random Forest, k-nearest-Neighbors, Support-Vector-Machine and Naïve-Bayes, applied to three different publicly available datasets from water testbeds. Specifically, we consider three different scenarios where we evaluate: (1) the ability to detect cyber and physical anomalies with respect to the nominal samples, (2) the ability to detect specific types of cyber and physical attacks and (3) the ability to recognize unforeseen attacks without providing any previous knowledge about them. Results show the effectiveness of the ML-techniques in identifying cyber and physical anomalies under some assumptions about their effects on the process dynamics.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2023. p. 412-417
Keywords [en]
Computer crime, Cyber Physical System, Embedded systems, Intrusion detection, Learning systems, Nearest neighbor search, SCADA systems, Anomaly based intrusion detection systems, Cybe-physical systems, Cyber-attacks, Cyber-physical systems, Industrial control systems, Machine learning approaches, Machine learning techniques, Performance comparison, Physical attacks, Still missing, Support vector machines
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-64444DOI: 10.1109/CSR57506.2023.10224915ISI: 001062143200064Scopus ID: 2-s2.0-85171731803ISBN: 9798350311709 (print)OAI: oai:DiVA.org:mdh-64444DiVA, id: diva2:1802764
Conference
Proceedings of the 2023 IEEE International Conference on Cyber Security and Resilience, CSR 2023
Available from: 2023-10-05 Created: 2023-10-05 Last updated: 2023-11-01Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Flammini, Francesco

Search in DiVA

By author/editor
Flammini, Francesco
By organisation
Innovation and Product Realisation
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 19 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf