SafeMan: A unified framework to manage cybersecurity and safety in manufacturing industry. (6th August 2020)
- Record Type:
- Journal Article
- Title:
- SafeMan: A unified framework to manage cybersecurity and safety in manufacturing industry. (6th August 2020)
- Main Title:
- SafeMan: A unified framework to manage cybersecurity and safety in manufacturing industry
- Authors:
- Perales Gómez, Ángel Luis
Fernández Maimó, Lorenzo
Huertas Celdrán, Alberto
García Clemente, Félix J.
Gil Pérez, Manuel
Martínez Pérez, Gregorio - Other Names:
- Baker Thar guestEditor.
Al‐Jumeily Dhiya guestEditor.
Maamar Zakaria guestEditor.
Tari Zahir guestEditor. - Abstract:
- Summary: Industrial control systems (ICS) are considered cyber‐physical systems that join both cyber and physical worlds. Due to their tight interaction, where humans and robots co‐work and co‐inhabit in the same workspaces and production lines, cyber‐attacks targeting ICS can alter production processes and even bypass safety procedures. As an example, these cyber‐attacks could interrupt physical industrial processes and cause potential injuries to workers. In this article, we present SafeMan, a unified management framework based on the Edge Computing paradigm that provides high‐performance applications for the detection and mitigation of both cyber‐attacks and safety threats in industrial scenarios. Three use cases show specific threats in manufacturing as well as the SafeMan actions carried out to detect and mitigate them. In order to validate our proposal, a pool of experiments was performed with Electra, an industrial dataset with normal network traffic and different cyber‐attacks by using a given number of Modbus TCP and S7Comm devices. The experiments measured the runtime performance of anomaly detection techniques based on machine learning and deep learning to detect cyber‐attacks in control networks. The experimental results show that Neural Networks report the best performance, being able to examine 2 17 feature vectors per second over Electra, and therefore demonstrating that it can be used as detection model for SafeMan in real scenarios.
- Is Part Of:
- Software, practice & experience. Volume 51:Number 3(2021)
- Journal:
- Software, practice & experience
- Issue:
- Volume 51:Number 3(2021)
- Issue Display:
- Volume 51, Issue 3 (2021)
- Year:
- 2021
- Volume:
- 51
- Issue:
- 3
- Issue Sort Value:
- 2021-0051-0003-0000
- Page Start:
- 607
- Page End:
- 627
- Publication Date:
- 2020-08-06
- Subjects:
- anomaly detection -- cybersecurity -- deep learning -- industrial control system -- machine learning -- safety
Computer software -- Periodicals
Computer programming -- Periodicals
Computer programs -- Periodicals
005.3 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1002/spe.2879 ↗
- Languages:
- English
- ISSNs:
- 0038-0644
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8321.453000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 15877.xml