AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure
The Internet of Things (IoT) is the most abundant technology in the fields of manufacturing, automation, transportation, robotics, and agriculture, utilizing the IoT’s sensors-sensing capability. It plays a vital role in digital transformation and smart revolutions in critical infrastructure environ...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/21/8928 |
| _version_ | 1797631243717705728 |
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| author | Tejal Rathod Nilesh Kumar Jadav Sudeep Tanwar Zdzislaw Polkowski Nagendar Yamsani Ravi Sharma Fayez Alqahtani Amr Gafar |
| author_facet | Tejal Rathod Nilesh Kumar Jadav Sudeep Tanwar Zdzislaw Polkowski Nagendar Yamsani Ravi Sharma Fayez Alqahtani Amr Gafar |
| author_sort | Tejal Rathod |
| collection | DOAJ |
| description | The Internet of Things (IoT) is the most abundant technology in the fields of manufacturing, automation, transportation, robotics, and agriculture, utilizing the IoT’s sensors-sensing capability. It plays a vital role in digital transformation and smart revolutions in critical infrastructure environments. However, handling heterogeneous data from different IoT devices is challenging from the perspective of security and privacy issues. The attacker targets the sensor communication between two IoT devices to jeopardize the regular operations of IoT-based critical infrastructure. In this paper, we propose an artificial intelligence (AI) and blockchain-driven secure data dissemination architecture to deal with critical infrastructure security and privacy issues. First, we reduced dimensionality using principal component analysis (PCA) and explainable AI (XAI) approaches. Furthermore, we applied different AI classifiers such as random forest (RF), decision tree (DT), support vector machine (SVM), perceptron, and Gaussian Naive Bayes (GaussianNB) that classify the data, i.e., malicious or non-malicious. Furthermore, we employ an interplanetary file system (IPFS)-driven blockchain network that offers security to the non-malicious data. In addition, to strengthen the security of AI classifiers, we analyze data poisoning attacks on the dataset that manipulate sensitive data and mislead the classifier, resulting in inaccurate results from the classifiers. To overcome this issue, we provide an anomaly detection approach that identifies malicious instances and removes the poisoned data from the dataset. The proposed architecture is evaluated using performance evaluation metrics such as accuracy, precision, recall, F1 score, and receiver operating characteristic curve (ROC curve). The findings show that the RF classifier transcends other AI classifiers in terms of accuracy, i.e., 98.46%. |
| first_indexed | 2024-03-11T11:21:04Z |
| format | Article |
| id | doaj.art-1c2cd3a954eb4ba588f6a083be45c407 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T11:21:04Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1c2cd3a954eb4ba588f6a083be45c4072023-11-10T15:12:48ZengMDPI AGSensors1424-82202023-11-012321892810.3390/s23218928AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical InfrastructureTejal Rathod0Nilesh Kumar Jadav1Sudeep Tanwar2Zdzislaw Polkowski3Nagendar Yamsani4Ravi Sharma5Fayez Alqahtani6Amr Gafar7Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, IndiaDepartment of Humanities and Social Sciences, The Karkonosze University of Applied Sciences, 58-506 Jelenia Galora, PolandDepartment of Computer Science and Artificial Intelligence, SR University, Warangal 506371, IndiaCentre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, P.O. Bidholi Via-Prem Nagar, Dehradun 248007, IndiaSoftware Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11437, Saudi ArabiaMathematics and Computer Science Department, Faculty of Science, Menofia University, Shebin Elkom 6131567, EgyptThe Internet of Things (IoT) is the most abundant technology in the fields of manufacturing, automation, transportation, robotics, and agriculture, utilizing the IoT’s sensors-sensing capability. It plays a vital role in digital transformation and smart revolutions in critical infrastructure environments. However, handling heterogeneous data from different IoT devices is challenging from the perspective of security and privacy issues. The attacker targets the sensor communication between two IoT devices to jeopardize the regular operations of IoT-based critical infrastructure. In this paper, we propose an artificial intelligence (AI) and blockchain-driven secure data dissemination architecture to deal with critical infrastructure security and privacy issues. First, we reduced dimensionality using principal component analysis (PCA) and explainable AI (XAI) approaches. Furthermore, we applied different AI classifiers such as random forest (RF), decision tree (DT), support vector machine (SVM), perceptron, and Gaussian Naive Bayes (GaussianNB) that classify the data, i.e., malicious or non-malicious. Furthermore, we employ an interplanetary file system (IPFS)-driven blockchain network that offers security to the non-malicious data. In addition, to strengthen the security of AI classifiers, we analyze data poisoning attacks on the dataset that manipulate sensitive data and mislead the classifier, resulting in inaccurate results from the classifiers. To overcome this issue, we provide an anomaly detection approach that identifies malicious instances and removes the poisoned data from the dataset. The proposed architecture is evaluated using performance evaluation metrics such as accuracy, precision, recall, F1 score, and receiver operating characteristic curve (ROC curve). The findings show that the RF classifier transcends other AI classifiers in terms of accuracy, i.e., 98.46%.https://www.mdpi.com/1424-8220/23/21/8928Internet of Thingscritical infrastructureartificial intelligencesecuritydata poisoning attacksblockchain network |
| spellingShingle | Tejal Rathod Nilesh Kumar Jadav Sudeep Tanwar Zdzislaw Polkowski Nagendar Yamsani Ravi Sharma Fayez Alqahtani Amr Gafar AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure Sensors Internet of Things critical infrastructure artificial intelligence security data poisoning attacks blockchain network |
| title | AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure |
| title_full | AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure |
| title_fullStr | AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure |
| title_full_unstemmed | AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure |
| title_short | AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure |
| title_sort | ai and blockchain based secure data dissemination architecture for iot enabled critical infrastructure |
| topic | Internet of Things critical infrastructure artificial intelligence security data poisoning attacks blockchain network |
| url | https://www.mdpi.com/1424-8220/23/21/8928 |
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