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THE ROLE OF INNOVATIVE TECHNOLOGIES IN THE PROTECTION OF CATEGORISED OBJECTS

Authors
  • Yakubov Jur’at Amangeldiyevich

    Ministry of Defense of the public of Uzbekistan
    Author
Keywords:
categorised objects, artificial intelligence, video analytics, biometric systems, IoT (Internet of Things), remote monitoring, cybersecurity, edge computing, access control, anomaly detection, digital security, smart technologies.
Abstract

This article analyses the theoretical and practical aspects of using innovative technologies to protect classified sites that require a high level of security, such as military facilities, strategic infrastructure, energy and transport systems. The possibilities, advantages and limitations of video analytics systems based on artificial intelligence, remote monitoring using Internet of Things (IoT) devices, and biometric identification systems are considered. Based on scientific sources, the study substantiates the effectiveness of real-time threat detection systems, anomaly detection, automated access control, and integrated security systems. It also proposes an architecture for creating a “smart security model” through the comprehensive integration of AI, IoT, and biometric systems, and highlights the measures necessary for such an approach in terms of cybersecurity, data privacy, and technical stability. Based on a systematic approach, the article reveals the role of modern digital security tools in protecting classified facilities.

References

Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials, 17(4), 2347–2376. course.ccs.neu.edu+2Elmi+2

Satyasree, K. P. N. V., Gongada, T. N., Upadhayaya, N., Naresh, E., Patra, J. P., & Kaur, M. (2023). Edge AI for Real-Time Video Analytics in Surveillance Systems. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 2269–2275. ijritcc.org

Edge-Computing-Enabled Abnormal Activity Recognition for Visual Surveillance. Electronics, 13(2), 251. (2024). MDPI

Zhang, R., & Yan, Z. (2019). A Survey on Biometric Authentication. IEEE Access, 7, 5994–6009. Aalto Doc+1

Yang, W., Wang, S., Sahri, N. M., Karie, N. M., Ahmed, M., & Valli, C. (2021). Biometrics for Internet-of-Things Security: A Review. Sensors, 21(18), 6163. MDPI+1

Sundararajan, A., Sarwat, A. I., & Pons, A. (2019). A Survey on Modality Characteristics, Performance Evaluation Metrics, and Security for Traditional and Wearable Biometric Systems. arXiv preprint. arXiv

Shamsoshoara, A., Korenda, A., Afghah, F., & Zeadally, S. (2019). A Survey on Physical Unclonable Function (PUF)-based Security Solutions for Internet of Things. arXiv preprint. arXiv

A Survey of Protocols and Standards for Internet of Things. arXiv preprint (Salman, T., & Jain, R.). (2020). arXiv

Qadir, Q. M., Rashid, T. A., Al-Salihi, N. K., Ismael, B., Kist, A. A., & Zhang, Z. (2020). Low Power Wide Area Networks: A Survey of Enabling Technologies, Applications and Interoperability Needs. arXiv preprint. arXiv

Comprehensive survey: Biometric user authentication application, evaluation, and discussion. Computers and Electrical Engineering, 119, 109485. (2024). ScienceDirect

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Published
2025-11-29
Section
Articles