AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure
Author:
Rathod Tejal1, Jadav Nilesh Kumar1ORCID, Tanwar Sudeep1ORCID, Polkowski Zdzislaw2, Yamsani Nagendar3, Sharma Ravi4, Alqahtani Fayez5ORCID, Gafar Amr6
Affiliation:
1. Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India 2. Department of Humanities and Social Sciences, The Karkonosze University of Applied Sciences, 58-506 Jelenia Galora, Poland 3. Department of Computer Science and Artificial Intelligence, SR University, Warangal 506371, India 4. Centre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, P.O. Bidholi Via-Prem Nagar, Dehradun 248007, India 5. Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11437, Saudi Arabia 6. Mathematics and Computer Science Department, Faculty of Science, Menofia University, Shebin Elkom 6131567, Egypt
Abstract
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%.
Funder
King Saud University
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference38 articles.
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