Abstract
•Device identification using several machine learning algorithms such as LR, KNN, SVC, DT, RF, GB, AdaBoost, LGBM, XGB, CNN, and LSTM.•Performance evaluation of models using multiple statistical parameters such as accuracy, precision, recall, F1 scores, etc.•Blockchain-based architecture for the smart home environment incorporating blockchain-based structure and smart contracts.•Blockchain-based architecture for two case studies, i.e., data marketplace and access control mechanism.
Due to data collection, there is a potential risk concerning security and privacy, so IoT reliability and survivability are of utmost concern. In this paper, we address the concern using two methods. The first method is device identification, which uses an extensive set of machine learning algorithms for identifying IoT devices. The algorithms include Logistic Regression, K- Nearest Neighbour, Support Vector Classifier, Random Forest, Gradient Boosting, AdaBoost, Light Gradient Boosting Machine, Extreme Gradient Boosting Convolution Neural Networks, and Long Short Term Memory are used for device identification. The performance of these models has been evaluated using multiple statistical measures, and weobserve that LSTM outperforms all other baseline models. The second method proposed for ensuring the survivability of theIoT environment is a blockchain-based architecture for smart homes to ensuretransparency and data protection.
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