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
Time-series Anomaly Detection and Classification with Long Short-Term Memory Network on Industrial Manufacturing Systems
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-4920-2012
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. RISE Research Institute of Sweden, Västerås, Sweden.ORCID iD: 0000-0001-5332-1033
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-3425-3837
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-4473-7763
Show others and affiliations
2023 (English)Report (Other (popular science, discussion, etc.))
Place, publisher, year, edition, pages
2023.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-62395OAI: oai:DiVA.org:mdh-62395DiVA, id: diva2:1755098
Available from: 2023-05-05 Created: 2023-05-05 Last updated: 2023-11-06Bibliographically approved
In thesis
1. Identification of Cyberattacks in Industrial Control Systems
Open this publication in new window or tab >>Identification of Cyberattacks in Industrial Control Systems
2023 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

As critical infrastructure increasingly relies on Industrial Control Systems (ICS), these systems have become a prime target for cyberattacks. As a result of the move towards Industry 4.0 targets, ICSs are increasingly being connected to the outside world, which makes them even more vulnerable to attacks. To enhance the ICS's security, Intrusion Detection Systems (IDS) are used in detecting and mitigating attacks. However, using real ICS installations for testing IDS can be challenging, as any interference with the ICS could have serious consequences, such as production downtime or compromised safety. Alternatively, ICS testbeds and cybersecurity datasets can be used to analyze, validate, and evaluate the IDS capabilities in a controlled environment. In addition, the complexity of ICSs, combined with the unpredictable and intricate nature of attacks, present a challenge in achieving high detection precision using traditional rule-based models. To tackle this challenge, Machine Learning (ML) have become increasingly attractive for identifying a broader range of attacks.

 

This thesis aims to enhance ICS cybersecurity by addressing the mentioned challenges. We introduce a framework for simulation of virtual ICS security testbeds that can be customized to create extensible, versatile, reproducible, and low-cost ICS testbeds. Using this framework, we create a factory simulation and its ICS to generate an ICS security dataset. We present this dataset as a validation benchmark for intrusion detection methods in ICSs. Finally, we investigate the efficiency and effectiveness of the intrusion detection capabilities of a range of Machine Learning techniques. Our findings show (1) that relying solely on intrusion evidence at a specific moment for intrusion detection can lead to misclassification, as various cyber-attacks may have similar effects at a specific moment, and (2) that AI models that consider the temporal relationship between events are effective in improving the ability to detect attack types.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2023
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 341
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-62403 (URN)978-91-7485-598-2 (ISBN)
Presentation
2023-06-16, Beta, Mälardalens universitet, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2023-05-09 Created: 2023-05-05 Last updated: 2023-11-06Bibliographically approved

Open Access in DiVA

No full text in DiVA

Authority records

Markovic, TijanaDehlaghi-Ghadim, AlirezaLeon, MiguelBalador, AliPunnekkat, Sasikumar

Search in DiVA

By author/editor
Markovic, TijanaDehlaghi-Ghadim, AlirezaLeon, MiguelBalador, AliPunnekkat, Sasikumar
By organisation
Embedded Systems
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 275 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