Intrusion Detection and Mitigation Method for the Industrial Internet of Things Using Bidirectional Convolutional Long Short-Term Memory and Deep Recurrent Convolutional Q-Networks
Abstract Cyber-physical system (CPS) security has become more important in the age of Industry 4.0 because of the quick integration of automation and the Internet of Things. The goal of this project is to create a strong intrusion detection and control system that can recognize and lessen security r...
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2025-06-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-025-00890-9 |
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| author | Zhang Yan Piyush Kumar Shukla Prashant Kumar Shukla Kanika Thakur Anurag Sinha Saifullah Khalid |
| author_facet | Zhang Yan Piyush Kumar Shukla Prashant Kumar Shukla Kanika Thakur Anurag Sinha Saifullah Khalid |
| author_sort | Zhang Yan |
| collection | DOAJ |
| description | Abstract Cyber-physical system (CPS) security has become more important in the age of Industry 4.0 because of the quick integration of automation and the Internet of Things. The goal of this project is to create a strong intrusion detection and control system that can recognize and lessen security risks in CPS settings. The suggested approach makes use of deep learning (DL) and reinforcement learning (RL) techniques. To guarantee data consistency, pre-processing procedures such as mean-based imputation and min–max scaling come after data collection. ADASYN data augmentation is used to address class imbalance, while entropy analysis and statistical techniques are used to extract key features. The intrusion detection phase uses a combination of deep convolutional neural networks (DCNN) and bidirectional long short-term memory (BI-LSTM) networks to capture both spatial and temporal relationships in the data, while a hybrid feature selection technique improves the model’s performance. A deep Q-network (DQN) handles attack mitigation and uses reinforcement learning to adjust to new threats. Detecting attack patterns with high sensitivity (0.984), specificity (0.983), and accuracy (0.991626) for dataset 1, the accuracy of dataset 2 is 0.985 for 70% of training and 0.988 for 80% of training, and the Proposed-DBID-Net architecture enhances CPS security in Industry 4.0. The evaluation phase emphasizes how crucial feature selection is to maximize the model’s accuracy. In conclusion, this study offers a thorough and flexible method for protecting CPS in Industry 4.0, guaranteeing accuracy and scalability across changing cyber threats. |
| format | Article |
| id | doaj-art-e33c43f379944cba8fce038f06fbb2aa |
| institution | OA Journals |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-e33c43f379944cba8fce038f06fbb2aa2025-08-20T02:10:32ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-06-0118113810.1007/s44196-025-00890-9Intrusion Detection and Mitigation Method for the Industrial Internet of Things Using Bidirectional Convolutional Long Short-Term Memory and Deep Recurrent Convolutional Q-NetworksZhang Yan0Piyush Kumar Shukla1Prashant Kumar Shukla2Kanika Thakur3Anurag Sinha4Saifullah Khalid5Law and Public Management School, Leshan Normal UniversityDepartment of Computer Science and Engineering, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (State Technological University of Madhya Pradesh)Professor, Department of Computer Science and Engineering & Deputy Dean Research(ASET), Amity School of Engineering and Technology (ASET), Amity UniversityDepartment of Computer Science and Engineering, Galgotias UniversityInstitute of Chartered Financial Analysts of India (ICFAI) UniversityIBMM ResearchAbstract Cyber-physical system (CPS) security has become more important in the age of Industry 4.0 because of the quick integration of automation and the Internet of Things. The goal of this project is to create a strong intrusion detection and control system that can recognize and lessen security risks in CPS settings. The suggested approach makes use of deep learning (DL) and reinforcement learning (RL) techniques. To guarantee data consistency, pre-processing procedures such as mean-based imputation and min–max scaling come after data collection. ADASYN data augmentation is used to address class imbalance, while entropy analysis and statistical techniques are used to extract key features. The intrusion detection phase uses a combination of deep convolutional neural networks (DCNN) and bidirectional long short-term memory (BI-LSTM) networks to capture both spatial and temporal relationships in the data, while a hybrid feature selection technique improves the model’s performance. A deep Q-network (DQN) handles attack mitigation and uses reinforcement learning to adjust to new threats. Detecting attack patterns with high sensitivity (0.984), specificity (0.983), and accuracy (0.991626) for dataset 1, the accuracy of dataset 2 is 0.985 for 70% of training and 0.988 for 80% of training, and the Proposed-DBID-Net architecture enhances CPS security in Industry 4.0. The evaluation phase emphasizes how crucial feature selection is to maximize the model’s accuracy. In conclusion, this study offers a thorough and flexible method for protecting CPS in Industry 4.0, guaranteeing accuracy and scalability across changing cyber threats.https://doi.org/10.1007/s44196-025-00890-9Cyber-physical systemsIndustry 4.0Intrusion detection and mitigationDeep learning (DL)Industrial internet of things |
| spellingShingle | Zhang Yan Piyush Kumar Shukla Prashant Kumar Shukla Kanika Thakur Anurag Sinha Saifullah Khalid Intrusion Detection and Mitigation Method for the Industrial Internet of Things Using Bidirectional Convolutional Long Short-Term Memory and Deep Recurrent Convolutional Q-Networks International Journal of Computational Intelligence Systems Cyber-physical systems Industry 4.0 Intrusion detection and mitigation Deep learning (DL) Industrial internet of things |
| title | Intrusion Detection and Mitigation Method for the Industrial Internet of Things Using Bidirectional Convolutional Long Short-Term Memory and Deep Recurrent Convolutional Q-Networks |
| title_full | Intrusion Detection and Mitigation Method for the Industrial Internet of Things Using Bidirectional Convolutional Long Short-Term Memory and Deep Recurrent Convolutional Q-Networks |
| title_fullStr | Intrusion Detection and Mitigation Method for the Industrial Internet of Things Using Bidirectional Convolutional Long Short-Term Memory and Deep Recurrent Convolutional Q-Networks |
| title_full_unstemmed | Intrusion Detection and Mitigation Method for the Industrial Internet of Things Using Bidirectional Convolutional Long Short-Term Memory and Deep Recurrent Convolutional Q-Networks |
| title_short | Intrusion Detection and Mitigation Method for the Industrial Internet of Things Using Bidirectional Convolutional Long Short-Term Memory and Deep Recurrent Convolutional Q-Networks |
| title_sort | intrusion detection and mitigation method for the industrial internet of things using bidirectional convolutional long short term memory and deep recurrent convolutional q networks |
| topic | Cyber-physical systems Industry 4.0 Intrusion detection and mitigation Deep learning (DL) Industrial internet of things |
| url | https://doi.org/10.1007/s44196-025-00890-9 |
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