AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-11, Vol.23 (21), p.8928
• Main Authors: Rathod, Tejal, Jadav, Nilesh Kumar, Tanwar, Sudeep, Polkowski, Zdzislaw, Yamsani, Nagendar, Sharma, Ravi, Alqahtani, Fayez, Gafar, Amr
• Format: Article
• Language: English
• Subjects: Accuracy; Analysis; Artificial intelligence; Blockchain; blockchain network; Communication; critical infrastructure; Data exchange; data poisoning attacks; Data security; Datasets; Digital transformation; Energy consumption; Industry 4.0; Infrastructure; Infrastructure (Economics); Internet of Things; Malware; Manufacturing; Network security; Nuclear energy; Nuclear power plants; Poisoning; Privacy; Privacy, Right of; Robotics; Safety and security measures; security; Sensors; Smart cities; Support vector machines; Water supply; Water treatment plants
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23218928

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%.