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Title	Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet Of Things In Smart City
ID_Doc	12275
Authors	Annadurai C.; Nelson I.; Devi K.N.; Manikandan R.; Jhanjhi N.Z.; Masud M.; Sheikh A.
Year	2022
Published	Energies, 15, 19
DOI	http://dx.doi.org/10.3390/en15197430
Abstract	Nowadays, there is a growing demand for information security and security rules all across the world. Intrusion detection (ID) is a critical technique for detecting dangers in a network during data transmission. Artificial Intelligence (AI) methods support the Internet of Things (IoT) and smart cities by creating gadgets replicating intelligent behavior and enabling decision making with little or no human intervention. This research proposes novel technique for secure data transmission and detecting an intruder in a biometric authentication system by feature extraction with classification. Here, an intruder is detected by collecting the biometric database of the smart building based on the IoT. These biometric data are processed for noise removal, smoothening, and normalization. The processed data features are extracted using the kernel-based principal component analysis (KPCA). Then, the processed features are classified using the convolutional VGG−16 Net architecture. Then, the entire network is secured using a deterministic trust transfer protocol (DTTP). The suggested technique’s performance was calculated utilizing several measures, such as the accuracy, f-score, precision, recall, and RMSE. The simulation results revealed that the proposed method provides better intrusion detection outcomes. © 2022 by the authors.
Author Keywords	artificial intelligence; biometric authentication; classification; feature extraction; intrusion detection; IoT

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