AI and Blockchain-Based Secure Data Dissemination Architecture for IoT

What is it about?

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%.

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Why is it important?

In this paper, we propose an AI and blockchain-driven intelligence anomaly detection architecture that provides security in the IoT-based critical infrastructure. Towards this aim, a standard dataset is utilized, which comprises data exchange between different IoT-based critical infrastructures, such as water treatment plants, nuclear power plants, and thermal plants. Furthermore, the dataset is pre-processed using diverse data pre-processing techniques that help enhance the overall performance of the proposed architecture. From the detailed analysis, we identified that there is a need to apply future selection methods to reduce the computation overhead caused by less important features and bulky datasets. Thus, we applied feature section techniques such as PCA and XAI that perform dimensionality reduction on the pre-processed dataset. Furthermore, we detect data poisoning attacks in the AI models that deteriorate the performance of AI training. As a result, AI classifiers such as RF, DT, SVM, perceptron, and GaussianNB classify the inaccurate results. To tackle this issue, we proposed anomaly detection that identifies the anomalous data from the feature space of the dataset and offers accurate results. The results show that the RF classifier achieves 98.46% accuracy compared with the other AI classifiers. Moreover, to enhance the security of the classified data, we used the IPFS-driven blockchain network, which offers secure storage of the critical infrastructure’s data. In future work, we will improvise the security implications in the IoT-based critical infrastructure by utilizing synthetic datasets, which will simulate in a Matlab environment. Furthermore, we will make a comparative analysis of our synthetic dataset results with the standard real-time results.

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