Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study
This work is concerned with the vulnerability of a network industrial control system to cyber-attacks, which is a critical issue nowadays. This is because an attack on a controlled process can damage or destroy it. These attacks use long short-term memory (LSTM) neural networks, which model dynamica...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/15/6778 |
| _version_ | 1797586045304307712 |
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| author | Krzysztof Zarzycki Patryk Chaber Krzysztof Cabaj Maciej Ławryńczuk Piotr Marusak Robert Nebeluk Sebastian Plamowski Andrzej Wojtulewicz |
| author_facet | Krzysztof Zarzycki Patryk Chaber Krzysztof Cabaj Maciej Ławryńczuk Piotr Marusak Robert Nebeluk Sebastian Plamowski Andrzej Wojtulewicz |
| author_sort | Krzysztof Zarzycki |
| collection | DOAJ |
| description | This work is concerned with the vulnerability of a network industrial control system to cyber-attacks, which is a critical issue nowadays. This is because an attack on a controlled process can damage or destroy it. These attacks use long short-term memory (LSTM) neural networks, which model dynamical processes. This means that the attacker may not know the physical nature of the process; an LSTM network is sufficient to mislead the process operator. Our experimental studies were conducted in an industrial control network containing a magnetic levitation process. The model training, evaluation, and structure selection are described. The chosen LSTM network very well mimicked the considered process. Finally, based on the obtained results, we formulated possible protection methods against the considered types of cyber-attack. |
| first_indexed | 2024-03-11T00:16:44Z |
| format | Article |
| id | doaj.art-ae1920a59f564d44baf5074cf0e6fa78 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:16:44Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ae1920a59f564d44baf5074cf0e6fa782023-11-18T23:34:20ZengMDPI AGSensors1424-82202023-07-012315677810.3390/s23156778Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case StudyKrzysztof Zarzycki0Patryk Chaber1Krzysztof Cabaj2Maciej Ławryńczuk3Piotr Marusak4Robert Nebeluk5Sebastian Plamowski6Andrzej Wojtulewicz7Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, PolandInstitute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, PolandInstitute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, PolandInstitute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, PolandInstitute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, PolandInstitute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, PolandInstitute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, PolandInstitute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, PolandThis work is concerned with the vulnerability of a network industrial control system to cyber-attacks, which is a critical issue nowadays. This is because an attack on a controlled process can damage or destroy it. These attacks use long short-term memory (LSTM) neural networks, which model dynamical processes. This means that the attacker may not know the physical nature of the process; an LSTM network is sufficient to mislead the process operator. Our experimental studies were conducted in an industrial control network containing a magnetic levitation process. The model training, evaluation, and structure selection are described. The chosen LSTM network very well mimicked the considered process. Finally, based on the obtained results, we formulated possible protection methods against the considered types of cyber-attack.https://www.mdpi.com/1424-8220/23/15/6778cyber-securitycyber-attacksLSTM neural networksindustrial control systemsSCADAPLC |
| spellingShingle | Krzysztof Zarzycki Patryk Chaber Krzysztof Cabaj Maciej Ławryńczuk Piotr Marusak Robert Nebeluk Sebastian Plamowski Andrzej Wojtulewicz Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study Sensors cyber-security cyber-attacks LSTM neural networks industrial control systems SCADA PLC |
| title | Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study |
| title_full | Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study |
| title_fullStr | Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study |
| title_full_unstemmed | Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study |
| title_short | Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study |
| title_sort | forgery cyber attack supported by lstm neural network an experimental case study |
| topic | cyber-security cyber-attacks LSTM neural networks industrial control systems SCADA PLC |
| url | https://www.mdpi.com/1424-8220/23/15/6778 |
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