Cyber-Physical System Security for Manufacturing Industry 4.0 Using LSTM-CNN Parallel Orchestration
Interoperability among different machines, systems, and humans connected via the Internet of Things (IoT) has blessed Industry 4.0 with numerous advantages over the years. However, these benefits have unleashed risks of cyber attacks on internet-connected manufacturing units such as autonomous intel...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10820346/ |
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| _version_ | 1841550834285936640 |
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| author | Salman Saeidlou Nikdokht Ghadiminia Kwadwo Oti-Sarpong |
| author_facet | Salman Saeidlou Nikdokht Ghadiminia Kwadwo Oti-Sarpong |
| author_sort | Salman Saeidlou |
| collection | DOAJ |
| description | Interoperability among different machines, systems, and humans connected via the Internet of Things (IoT) has blessed Industry 4.0 with numerous advantages over the years. However, these benefits have unleashed risks of cyber attacks on internet-connected manufacturing units such as autonomous intelligent computer-controlled cutting (ICNC) machines. These are used in different manufacturing industries to ensure high precision and faster production. Over the Internet these machines receive product designs and instructions of how to produce them. Intrusions through malicious code embedded in the design can hamper precision and cause production delays, resulting in significant revenue loss. This paper presents an innovative cyber-physical system (CPS) security mechanism, using a long short-term memory (LSTM) network and a convolutional neural network (CNN) coordinated by a parallel orchestration (PLO) algorithm. It detects intrusions from both image and text data with 90.85% and 91.66% accuracy, respectively. Applying the proposed methodology in a simulated manufacturing industry shows an average yearly successful intrusion reduction from 184 to 15, saving an average of 30,474 in revenue. Its innovative concept, the distinctive mechanism of the PLO algorithm, and applying it in a simulated manufacturing industry make the proposed security system superior to comparable approaches. |
| format | Article |
| id | doaj-art-446f303d6f8d4928928673a45ec381dc |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-446f303d6f8d4928928673a45ec381dc2025-01-10T00:00:51ZengIEEEIEEE Access2169-35362025-01-01133788380110.1109/ACCESS.2025.352552010820346Cyber-Physical System Security for Manufacturing Industry 4.0 Using LSTM-CNN Parallel OrchestrationSalman Saeidlou0https://orcid.org/0000-0002-3235-6592Nikdokht Ghadiminia1https://orcid.org/0000-0003-2408-8046Kwadwo Oti-Sarpong2School of Engineering, Technology and Design, Canterbury Christ Church University, Canterbury, Kent, U.K.Old Royal Naval College, University of Greenwich, London, U.K.Department of Engineering, Centre for Smart Infrastructure and Construction, University of Cambridge, Cambridge, U.K.Interoperability among different machines, systems, and humans connected via the Internet of Things (IoT) has blessed Industry 4.0 with numerous advantages over the years. However, these benefits have unleashed risks of cyber attacks on internet-connected manufacturing units such as autonomous intelligent computer-controlled cutting (ICNC) machines. These are used in different manufacturing industries to ensure high precision and faster production. Over the Internet these machines receive product designs and instructions of how to produce them. Intrusions through malicious code embedded in the design can hamper precision and cause production delays, resulting in significant revenue loss. This paper presents an innovative cyber-physical system (CPS) security mechanism, using a long short-term memory (LSTM) network and a convolutional neural network (CNN) coordinated by a parallel orchestration (PLO) algorithm. It detects intrusions from both image and text data with 90.85% and 91.66% accuracy, respectively. Applying the proposed methodology in a simulated manufacturing industry shows an average yearly successful intrusion reduction from 184 to 15, saving an average of 30,474 in revenue. Its innovative concept, the distinctive mechanism of the PLO algorithm, and applying it in a simulated manufacturing industry make the proposed security system superior to comparable approaches.https://ieeexplore.ieee.org/document/10820346/Cyber-physical systemsInternet of ThingsIndustry 4.0LSTMCNNintrusion detection |
| spellingShingle | Salman Saeidlou Nikdokht Ghadiminia Kwadwo Oti-Sarpong Cyber-Physical System Security for Manufacturing Industry 4.0 Using LSTM-CNN Parallel Orchestration IEEE Access Cyber-physical systems Internet of Things Industry 4.0 LSTM CNN intrusion detection |
| title | Cyber-Physical System Security for Manufacturing Industry 4.0 Using LSTM-CNN Parallel Orchestration |
| title_full | Cyber-Physical System Security for Manufacturing Industry 4.0 Using LSTM-CNN Parallel Orchestration |
| title_fullStr | Cyber-Physical System Security for Manufacturing Industry 4.0 Using LSTM-CNN Parallel Orchestration |
| title_full_unstemmed | Cyber-Physical System Security for Manufacturing Industry 4.0 Using LSTM-CNN Parallel Orchestration |
| title_short | Cyber-Physical System Security for Manufacturing Industry 4.0 Using LSTM-CNN Parallel Orchestration |
| title_sort | cyber physical system security for manufacturing industry 4 0 using lstm cnn parallel orchestration |
| topic | Cyber-physical systems Internet of Things Industry 4.0 LSTM CNN intrusion detection |
| url | https://ieeexplore.ieee.org/document/10820346/ |
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