Possible Ways to Improve Privacy in IoT Networks
DOI:
https://doi.org/10.32515/2664-262X.2025.11(42).1.46-55Keywords:
IoT network, artificial intelligence, machine learning, information securityAbstract
The rapid development of IoT technologies and their integration into everyday life are leading to a significant increase in the amount of data collected, transmitted and processed. This creates significant risks of confidential information leakage, which can have serious consequences for individual users, businesses and government agencies. In addition, the growing number of connected devices and their interaction in global networks increase the vulnerability of systems to cyberattacks, which can lead to unauthorized access to critical data.
The purpose of this article is to analyze the vulnerabilities of the Internet of Things (IoT) and to consider modern methods of detecting and counteracting cyber threats in such networks. The main objectives of the study include: Identification of the main threats and vulnerabilities of IoT networks at different levels of interaction; Analysis of modern attack methods applied to IoT systems and their consequences; Overview and classification of security methods, including cryptographic mechanisms, blockchain solutions, artificial intelligence-based anomaly detection systems, etc; Comparison of the effectiveness of different approaches to cybersecurity of IoT infrastructure and identification of their advantages and disadvantages; Formulating recommendations for the implementation of more reliable mechanisms to protect IoT networks.
Based on the analysis, the article proposes promising approaches to improving the security of IoT infrastructure, which can be used to minimize risks and improve the protection of users' personal information. The results of the study can be useful both for scientists dealing with cybersecurity issues and for practitioners working in the field of development and implementation of IoT solutions
References
Список літератури
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References
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2. California Consumer Privacy Act (CCPA). (2024, March 13). Retrieved February 15, 2025, from https://oag.ca.gov/privacy/ccpa.
3. Pinto, G. P., Donta, P. K., Dustdar, S., & Prazeres, C. (2024). A systematic review on privacy-aware IoT personal data stores. Sensors, 24(7), 2197. Retrieved February 10, 2025, from https://pmc.ncbi.nlm.nih.gov/articles/PMC11014407/.
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6. Lu, Y. (2023). Security and privacy of Internet of Things: A review of challenges and solutions. Journal of Cyber Security and Mobility, 12(6). Retrieved February 15, 2025, from https://journals.riverpublishers.com/index.php/JCSANDM/article/view/22587/.
7. Abomhara, M., & Køien, G. M. (2014). Security and privacy in the Internet of Things: Current status and open issues. Proceedings of the 2014 International Conference on Privacy and Security in Mobile Systems (PRISMS). https://doi.org/10.1109/PRISMS.2014.6970594.
8. Pinto, G. P., Donta, P. K., & Dustdar, S. (2024). A systematic review on privacy-aware IoT personal data stores. Sensors, 24(7), 2197. Retrieved February 15, 2025, from https://pubmed.ncbi.nlm.nih.gov/38610408
9. Ystgaard, K. F., Atzori, L., Palma, D., et al. (2023). Review of the theory, principles, and design requirements of human-centric Internet of Things (IoT). Journal of Ambient Intelligence and Humanized Computing, 14, 2827–2859. Retrieved February 15, 2025, from link.springer.com/article/10.1007/s12652-023-04539-3.
10. Sicari, S., Rizzardi, A., Grieco, L. A., & Coen-Porisini, A. (2015). Security, privacy and trust in Internet of Things: The road ahead. Computer Networks, 76, 146–164. Retrieved February 15, 2025, from https://www.sciencedirect.com/science/article/abs/pii/S1389128614003971.
11. Roman, R., Zhou, J., & López, J. (2013). On the features and challenges of security and privacy in distributed Internet of Things. Computer Networks, 57(10), 2266–2279. Retrieved February 15, 2025, from https://www.sciencedirect.com/science/article/abs/pii/S1389128613000054.
12. Ben Henda, N., Msolli, A., Hagui, I., & Helali, A. (2024). Attack detection in IoT network using support vector machine and improved feature selection technique. Journal of Network and Systems Management, 32(4). https://doi.org/10.1007/s10922-024-09871-3.
13. Meidan, Y., Bohadana, M., Mathov, Y., et al. (2018). Network-based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Computing, 17(3). Retrieved February 15, 2025, from https://ieeexplore.ieee.org/document/8490192.
14. Ul Haque, E., Abbasi, W., Almogren, A., Choi, J., et al. (2024). Performance enhancement in blockchain-based IoT data sharing using lightweight consensus algorithm. Scientific Reports, 14, Article 26561. Retrieved February 20, 2025, from https://www.nature.com/articles/s41598-024-77706-x.
15. Jiang, B., Li, J., Yue, G., & Song, H. (2021). Differential privacy for industrial Internet of Things: Opportunities, applications and challenges. IEEE Internet of Things Journal, 8(13), 10430–10451. Retrieved February 20, 2025, from https://arxiv.org/abs/2101.10569.
16. Chauhan, K. K., Sanger, A. K. S., & Verm, A. (2015). Homomorphic encryption for data security in cloud computing. Proceedings of the International Conference on Information Technology (ICIT). https://doi.org/10.1109/ICIT.2015.39.
17. Rose, S., Borchert, O., Mitchell, S., & Connelly, S. (2020). Zero trust architecture. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-207.
18. Alkhonaini, M. A., Alenizi, F. A., Jazyah, Y. H., & Lee, S. (2024). A two-phase spatiotemporal chaos-based protocol for data integrity in IoT. Scientific Reports, 14, Article 8629. Retrieved February 14, 2025, from https://www.nature.com/articles/s41598-024-58914-x.
19. Baker, A., & River, W. (n.d.). Maintaining data integrity in Internet of Things applications. Retrieved February 14, 2025, from https://files.iccmedia.com/pdf/windriver160823.pdf.
20. Amos, Z. (n.d.). Multi-factor authentication is crucial for IoT security. Retrieved February 19, 2025, from https://www.iotforall.com/multi-factor-authentication-is-crucial-for-iot-security.
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