SAINT-IIOT: Elk herd optimized deep learning model for efficient anomaly detection in the IIoT
Industrial Internet of Things (IIoT) is an innovative technology that may mitigate manufacturing costs, increase production efficiency, and foster the growth of industrial intelligence. IIoT applications face security and privacy risks as a result of IIoT device abnormalities reveal sensitive inform...
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| Veröffentlicht in: | Ain Shams Engineering Journal Jg. 16; H. 12; S. 103625 |
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01.12.2025
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| Abstract | Industrial Internet of Things (IIoT) is an innovative technology that may mitigate manufacturing costs, increase production efficiency, and foster the growth of industrial intelligence. IIoT applications face security and privacy risks as a result of IIoT device abnormalities reveal sensitive information with high authenticity and validity. To address these issues, a novel cascaded Stacked Autoencoder INtegrated aTtention CNN-BiGRU for IIoT (SAINT-IIoT) model has been proposed in this paper to improve the real-time detection of cyber threats in IIoT environments. The proposed methodology employs an Elk Herd Optimization (EHO) algorithm for effectively selecting the features, which address the issue of irrelevant and noisy features. The Deep Learning (DL) technique is used for real-time anomaly classification to handle complex, nonlinear, and time-dependent attack patterns that traditional models often fail to identify. The accuracy of the suggested framework is 7.04%, 12.11%, and 3.26% higher than the existing techniques including DRL-GAN, AIm-ADS, and EPOA-EVAD. |
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| AbstractList | Industrial Internet of Things (IIoT) is an innovative technology that may mitigate manufacturing costs, increase production efficiency, and foster the growth of industrial intelligence. IIoT applications face security and privacy risks as a result of IIoT device abnormalities reveal sensitive information with high authenticity and validity. To address these issues, a novel cascaded Stacked Autoencoder INtegrated aTtention CNN-BiGRU for IIoT (SAINT-IIoT) model has been proposed in this paper to improve the real-time detection of cyber threats in IIoT environments. The proposed methodology employs an Elk Herd Optimization (EHO) algorithm for effectively selecting the features, which address the issue of irrelevant and noisy features. The Deep Learning (DL) technique is used for real-time anomaly classification to handle complex, nonlinear, and time-dependent attack patterns that traditional models often fail to identify. The accuracy of the suggested framework is 7.04%, 12.11%, and 3.26% higher than the existing techniques including DRL-GAN, AIm-ADS, and EPOA-EVAD. |
| ArticleNumber | 103625 |
| Author | Jaison, B. Mahalakshmi, K. |
| Author_xml | – sequence: 1 givenname: K. surname: Mahalakshmi fullname: Mahalakshmi, K. email: mahalakshmi1586@gmail.com organization: Department of Computer Science and Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu 600062, India – sequence: 2 givenname: B. surname: Jaison fullname: Jaison, B. email: bjn.cse@rmkec.ac.in organization: Department of Computer Science and Engineering, R.M.K. Engineering College, Kavaraipettai, Tamil Nadu 601206, India |
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| Cites_doi | 10.1016/j.eswa.2024.123808 10.3390/s25072146 10.1016/j.future.2023.06.011 10.1007/s10586-024-04303-y 10.1109/TII.2022.3224981 10.1016/j.cose.2024.104130 10.1007/s10586-023-04163-y 10.3390/s22218085 10.1109/TNSM.2024.3447532 10.1007/s10586-024-04529-w 10.1038/s41598-023-46746-0 10.1016/j.neunet.2025.107400 10.1016/j.procs.2023.01.153 10.1109/JIOT.2024.3384374 10.1016/j.iot.2024.101446 10.1016/j.cose.2023.103174 10.3390/app15063121 |
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| Keywords | Deep learning Synthetic minority over-sampling technique Elk Herd optimization Cascaded stacked autoencoder Anomaly detection |
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| SubjectTerms | Anomaly detection Cascaded stacked autoencoder Deep learning Elk Herd optimization Synthetic minority over-sampling technique |
| Title | SAINT-IIOT: Elk herd optimized deep learning model for efficient anomaly detection in the IIoT |
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