An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems
Abstract The Industrial Internet of Things (IIoT) promises to deliver innovative business models across multiple domains by providing ubiquitous connectivity, intelligent data, predictive analytics, and decision-making systems for improved market performance. However, traditional IIoT architectures...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-03-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-023-00412-y |
| _version_ | 1797863575884136448 |
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| author | Shitharth Selvarajan Gautam Srivastava Alaa O. Khadidos Adil O. Khadidos Mohamed Baza Ali Alshehri Jerry Chun-Wei Lin |
| author_facet | Shitharth Selvarajan Gautam Srivastava Alaa O. Khadidos Adil O. Khadidos Mohamed Baza Ali Alshehri Jerry Chun-Wei Lin |
| author_sort | Shitharth Selvarajan |
| collection | DOAJ |
| description | Abstract The Industrial Internet of Things (IIoT) promises to deliver innovative business models across multiple domains by providing ubiquitous connectivity, intelligent data, predictive analytics, and decision-making systems for improved market performance. However, traditional IIoT architectures are highly susceptible to many security vulnerabilities and network intrusions, which bring challenges such as lack of privacy, integrity, trust, and centralization. This research aims to implement an Artificial Intelligence-based Lightweight Blockchain Security Model (AILBSM) to ensure privacy and security of IIoT systems. This novel model is meant to address issues that can occur with security and privacy when dealing with Cloud-based IIoT systems that handle data in the Cloud or on the Edge of Networks (on-device). The novel contribution of this paper is that it combines the advantages of both lightweight blockchain and Convivial Optimized Sprinter Neural Network (COSNN) based AI mechanisms with simplified and improved security operations. Here, the significant impact of attacks is reduced by transforming features into encoded data using an Authentic Intrinsic Analysis (AIA) model. Extensive experiments are conducted to validate this system using various attack datasets. In addition, the results of privacy protection and AI mechanisms are evaluated separately and compared using various indicators. By using the proposed AILBSM framework, the execution time is minimized to 0.6 seconds, the overall classification accuracy is improved to 99.8%, and detection performance is increased to 99.7%. Due to the inclusion of auto-encoder based transformation and blockchain authentication, the anomaly detection performance of the proposed model is highly improved, when compared to other techniques. |
| first_indexed | 2024-04-09T22:37:42Z |
| format | Article |
| id | doaj.art-f5da395c02194340803ab88692440cef |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-04-09T22:37:42Z |
| publishDate | 2023-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-f5da395c02194340803ab88692440cef2023-03-22T12:23:27ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2023-03-0112111710.1186/s13677-023-00412-yAn artificial intelligence lightweight blockchain security model for security and privacy in IIoT systemsShitharth Selvarajan0Gautam Srivastava1Alaa O. Khadidos2Adil O. Khadidos3Mohamed Baza4Ali Alshehri5Jerry Chun-Wei Lin6Department of Computer Science, Kebri Dehar UniversityDepartment of Math and Computer Science, Brandon UniversityDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz UniversityDepartment of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz UniversityDepartment of Computer Science, College of CharlestonDepartment of Computer Science, University of TabukDepartment of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied SciencesAbstract The Industrial Internet of Things (IIoT) promises to deliver innovative business models across multiple domains by providing ubiquitous connectivity, intelligent data, predictive analytics, and decision-making systems for improved market performance. However, traditional IIoT architectures are highly susceptible to many security vulnerabilities and network intrusions, which bring challenges such as lack of privacy, integrity, trust, and centralization. This research aims to implement an Artificial Intelligence-based Lightweight Blockchain Security Model (AILBSM) to ensure privacy and security of IIoT systems. This novel model is meant to address issues that can occur with security and privacy when dealing with Cloud-based IIoT systems that handle data in the Cloud or on the Edge of Networks (on-device). The novel contribution of this paper is that it combines the advantages of both lightweight blockchain and Convivial Optimized Sprinter Neural Network (COSNN) based AI mechanisms with simplified and improved security operations. Here, the significant impact of attacks is reduced by transforming features into encoded data using an Authentic Intrinsic Analysis (AIA) model. Extensive experiments are conducted to validate this system using various attack datasets. In addition, the results of privacy protection and AI mechanisms are evaluated separately and compared using various indicators. By using the proposed AILBSM framework, the execution time is minimized to 0.6 seconds, the overall classification accuracy is improved to 99.8%, and detection performance is increased to 99.7%. Due to the inclusion of auto-encoder based transformation and blockchain authentication, the anomaly detection performance of the proposed model is highly improved, when compared to other techniques.https://doi.org/10.1186/s13677-023-00412-yArtificial intelligenceBlockchainConvivial Optimized Sprinter Neural NetworkCloud computingFog computingSecurity |
| spellingShingle | Shitharth Selvarajan Gautam Srivastava Alaa O. Khadidos Adil O. Khadidos Mohamed Baza Ali Alshehri Jerry Chun-Wei Lin An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems Journal of Cloud Computing: Advances, Systems and Applications Artificial intelligence Blockchain Convivial Optimized Sprinter Neural Network Cloud computing Fog computing Security |
| title | An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems |
| title_full | An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems |
| title_fullStr | An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems |
| title_full_unstemmed | An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems |
| title_short | An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems |
| title_sort | artificial intelligence lightweight blockchain security model for security and privacy in iiot systems |
| topic | Artificial intelligence Blockchain Convivial Optimized Sprinter Neural Network Cloud computing Fog computing Security |
| url | https://doi.org/10.1186/s13677-023-00412-y |
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