Internet of things for smart manufacturing based on advanced encryption standard (AES) algorithm with chaotic system
Smart manufacturing using the Internet of Things (IoT) ensures uninterrupted and human intervention-less automation in industries for precision outcomes. As the smart manufacturing encloses chaotic systems the point of security is always demandable due to external threats. For mitigating the authori...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123023007168 |
| _version_ | 1797385131175968768 |
|---|---|
| author | Xiaoyan Huo Xuemei Wang |
| author_facet | Xiaoyan Huo Xuemei Wang |
| author_sort | Xiaoyan Huo |
| collection | DOAJ |
| description | Smart manufacturing using the Internet of Things (IoT) ensures uninterrupted and human intervention-less automation in industries for precision outcomes. As the smart manufacturing encloses chaotic systems the point of security is always demandable due to external threats. For mitigating the authorization issues in chaotic systems, a Smart Reviving Authorization Model using Advanced Encryption Standard (SRAM-AES) is designed in this article. This model is selective for chaotic systems for reviving their conventional operation cycles and preventing failures. A machine/controller’s performance is monitored for its point of instability through differential access. The malicious access and its cause for controller unstableness are verified using IoT elements (remotely) and deep recurrent learning algorithms. Such identified instances are recovered by providing alternate controller recommendations from the IoT platform. In the recurrent learning process, the unstable to stable point possibilities are verified; the passing controllers are equipped with AES mitigating the previous authorizations. For a stable-functioning controller, the AES deficiency in authorization is verified in its completion cycles for consecutive production instances. Thus this model stands reliable for preventing unauthorized access, controller downtime reduction, and production failures. |
| first_indexed | 2024-03-08T21:49:43Z |
| format | Article |
| id | doaj.art-13a1f13f645b4932aed18d924ac30da0 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-03-08T21:49:43Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-13a1f13f645b4932aed18d924ac30da02023-12-20T07:36:18ZengElsevierResults in Engineering2590-12302023-12-0120101589Internet of things for smart manufacturing based on advanced encryption standard (AES) algorithm with chaotic systemXiaoyan Huo0Xuemei Wang1Information Construction and Management Center, Jiaozuo University, Jiaozuo, 454003, China; Corresponding author.Academic Affairs Division, Jiaozuo Technical College, Jiaozuo, 454000, ChinaSmart manufacturing using the Internet of Things (IoT) ensures uninterrupted and human intervention-less automation in industries for precision outcomes. As the smart manufacturing encloses chaotic systems the point of security is always demandable due to external threats. For mitigating the authorization issues in chaotic systems, a Smart Reviving Authorization Model using Advanced Encryption Standard (SRAM-AES) is designed in this article. This model is selective for chaotic systems for reviving their conventional operation cycles and preventing failures. A machine/controller’s performance is monitored for its point of instability through differential access. The malicious access and its cause for controller unstableness are verified using IoT elements (remotely) and deep recurrent learning algorithms. Such identified instances are recovered by providing alternate controller recommendations from the IoT platform. In the recurrent learning process, the unstable to stable point possibilities are verified; the passing controllers are equipped with AES mitigating the previous authorizations. For a stable-functioning controller, the AES deficiency in authorization is verified in its completion cycles for consecutive production instances. Thus this model stands reliable for preventing unauthorized access, controller downtime reduction, and production failures.http://www.sciencedirect.com/science/article/pii/S2590123023007168AESChaotic systemIoTRecurrent learningSmart manufacturing |
| spellingShingle | Xiaoyan Huo Xuemei Wang Internet of things for smart manufacturing based on advanced encryption standard (AES) algorithm with chaotic system Results in Engineering AES Chaotic system IoT Recurrent learning Smart manufacturing |
| title | Internet of things for smart manufacturing based on advanced encryption standard (AES) algorithm with chaotic system |
| title_full | Internet of things for smart manufacturing based on advanced encryption standard (AES) algorithm with chaotic system |
| title_fullStr | Internet of things for smart manufacturing based on advanced encryption standard (AES) algorithm with chaotic system |
| title_full_unstemmed | Internet of things for smart manufacturing based on advanced encryption standard (AES) algorithm with chaotic system |
| title_short | Internet of things for smart manufacturing based on advanced encryption standard (AES) algorithm with chaotic system |
| title_sort | internet of things for smart manufacturing based on advanced encryption standard aes algorithm with chaotic system |
| topic | AES Chaotic system IoT Recurrent learning Smart manufacturing |
| url | http://www.sciencedirect.com/science/article/pii/S2590123023007168 |
| work_keys_str_mv | AT xiaoyanhuo internetofthingsforsmartmanufacturingbasedonadvancedencryptionstandardaesalgorithmwithchaoticsystem AT xuemeiwang internetofthingsforsmartmanufacturingbasedonadvancedencryptionstandardaesalgorithmwithchaoticsystem |