Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City

C. Annadurai, I. Nelson, K. Nirmala Devi, R. Manikandan, Nz Jhanjhi (), Mehedi Masud and Abdullah Sheikh
Additional contact information
C. Annadurai: Department of ECE, Sri Sivasubramaniya Nadar College of Engineering, Chennai 603110, Tamilnadu, India
I. Nelson: Department of ECE, Sri Sivasubramaniya Nadar College of Engineering, Chennai 603110, Tamilnadu, India
K. Nirmala Devi: Department of CSE, Kongu Engineering College, Erode 638060, Tamilnadu, India
R. Manikandan: School of Computing, SASTRA Deemed University, Thanjavur 613401, Tamilnadu, India
Mehedi Masud: Department of Computer Science, College of Computer and Information Technology, Taif University, Taif 26571, Saudi Arabia
Abdullah Sheikh: Department of Computer Science, College of Computer and Information Technology, Taif University, Taif 26571, Saudi Arabia

Energies, 2022, vol. 15, issue 19, 1-14

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.

Keywords: intrusion detection; artificial intelligence; IoT; biometric authentication; feature extraction; classification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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