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

Author

Listed:
  • 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)

  • N. Z. Jhanjhi

    (School of Computer Science, SCS, Taylor’s University, Subang Jaya 47500, Selangor, Malaysia)

  • 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)

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.

Suggested Citation

  • C. Annadurai & I. Nelson & K. Nirmala Devi & R. Manikandan & N. Z. Jhanjhi & Mehedi Masud & Abdullah Sheikh, 2022. "Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City," Energies, MDPI, vol. 15(19), pages 1-14, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7430-:d:937856
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    References listed on IDEAS

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    1. Farhad Ahamed & Farnaz Farid & Basem Suleiman & Zohaib Jan & Luay A. Wahsheh & Seyed Shahrestani, 2022. "An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services," Future Internet, MDPI, vol. 14(8), pages 1-28, July.
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    Cited by:

    1. Raval, Khushi Jatinkumar & Jadav, Nilesh Kumar & Rathod, Tejal & Tanwar, Sudeep & Vimal, Vrince & Yamsani, Nagendar, 2024. "A survey on safeguarding critical infrastructures: Attacks, AI security, and future directions," International Journal of Critical Infrastructure Protection, Elsevier, vol. 44(C).

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