SYSTEMATIC ANALYSIS OF INFORMATION SECURITY PROVISION METHODS IN IOT DEVICES
DOI:
https://doi.org/10.28925/2663-4023.2026.34.1226Keywords:
authentication, hardware protection methods, blockchain, Internet of Things, information security, cryptography, artificial intelligenceAbstract
This study examines well-known methods of ensuring information security that are relevant for IoT devices and take into account their specifics. The relevance of the problem of information protection in IoT is substantiated, a wide range of existing approaches is described and the need for their comprehensive application, taking into account the resource limitations of devices, is analyzed. The scientific works of researchers covering lightweight cryptography, intrusion detection systems, hardware protection methods, blockchain and artificial intelligence methods are analyzed. The four-level architecture of IoT systems (device level, network, platform and application level) is considered from the point of view of threats and protection mechanisms, and the conceptual principles of each method are considered in detail: lightweight cryptography (PRESTENT, SPECK, SIMON, ASCON algorithms), intrusion detection systems (IDS) based on signatures, anomalies and machine learning, hardware protection means (TPM, Secure Boot, TEE, PUF), blockchain technology and methods using artificial intelligence. For each method, the advantages and limitations are systematized, taking into account the specifics of resource-limited IoT devices. In the final section, a comparative analysis of the specified methods is carried out according to five criteria: data protection efficiency, attack detection, adaptability, resource costs and optimal level of use in the IoT architecture. The scientific novelty of the work lies in the systematization of the specified methods in a single comparative matrix, taking into account the optimal level of their application in the IoT architecture. Based on the conducted research, the feasibility of using a comprehensive approach to ensuring information security, which involves the integration of various protection mechanisms at all levels of the IoT architecture, is substantiated.
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Amrita, Ekwueme, C. P., Adam, I. H., & Dwivedi, A. (2024). Lightweight cryptography for Internet of Things: A review. EAI Endorsed Transactions on Internet of Things, 10. https://publications.eai.eu/index.php/IoT/article/view/5565
Suryateja, P. S., & Rao, K. V. (2024). A survey on lightweight cryptographic algorithms in IoT. Cybernetics and Information Technologies, 24(1), 21–34. https://doi.org/10.2478/cait-2024-0002
Alqahtani, H., & Sarker, I. H. (2025). A systematic review of lightweight cryptographic schemes for security and privacy in IoT. Discover Computing. https://doi.org/10.1007/s10791-025-09755-3
Santos-González, I., Rivero-García, A., González-Martín, C., & Caballero-Gil, P. (2025). A survey of efficient lightweight cryptography for power-constrained microcontrollers. Technologies, 13(1), 3. https://doi.org/10.3390/technologies13010003
Bhavsar, M., Roy, K., Kelly, J., & Odeyomi, O. (2023). Anomaly-based intrusion detection system for IoT application. Discover Internet of Things, 3, Article 5. https://doi.org/10.1007/s43926-023-00034-5
Sharma, B., Sharma, L., Lal, C., & Roy, S. (2023). Anomaly based network intrusion detection for IoT attacks using deep learning technique. Computers and Electrical Engineering, 107, Article 108626. https://doi.org/10.1016/j.compeleceng.2023.108626
Sabeena, S., & Chitra, S. (2024). Comparative study on anomaly based intrusion detection using deep learning techniques. EAI Endorsed Transactions on Internet of Things, 11(1). https://doi.org/10.4108/eetiot.7178
Seth, A. (2025). Attack and anomaly detection in IoT sensors using machine learning approaches. Journal of Recent Innovations in Computer Science and Technology, 2(1), 16–27. https://doi.org/10.70454/JRICST.2025.20108
Mohan, S., Jayakumar, V., & Selvaraj, R. (2025). TinyML-based intrusion detection systems for sustainable and energy-constrained IoT devices. Results in Engineering, 24, 103171. https://doi.org/10.1016/j.rineng.2025.108013
Velázquez-Rodríguez, A., Iglesias-Martínez, M. E., & Fernández-de-Vega, F. (2025). Lightweight signal processing and edge AI for real-time anomaly detection in IoT sensor networks. Sensors, 25(21), 6629. https://doi.org/10.3390/s25216629
Xia, Y., Zhou, Y., Liu, J. N., Liu, X., Chen, J., Xiao, F., & Zhu, H. (2025). Hardware-enhanced data security for Internet of Things. Fundamental Research. https://doi.org/10.1016/j.fmre.2025.09.025
Zhurylo, O., Liashenko, O., & Avetisova, K. (2023). Hardware security overview of fog computing end devices in the Internet of Things. Innovative Technologies and Scientific Solutions for Industries, 1(23), 57–71. https://doi.org/10.30837/ITSSI.2023.23.057
García-Teodoro, P., Camacho, J., Maciá-Fernández, G., & Díaz-Verdejo, J. E. (2024). Strengthening Internet of Things security: Surveying physical unclonable functions for authentication, communication protocols, challenges, and applications. Applied Sciences, 14(5), 1700. https://doi.org/10.3390/app14051700
Almarri, S., & Aljughaiman, A. (2024). Blockchain technology for IoT security and trust: A comprehensive SLR. Sustainability, 16(23), 10177. https://doi.org/10.3390/su162310177
Gugueoth, V., Safavat, S., Shetty, S., & Rawat, D. (2023). A review of IoT security and privacy using decentralized blockchain techniques. Computer Science Review, 50, 100585. https://doi.org/10.1016/j.cosrev.2023.100585
Obaidat, M. A., Rawashdeh, M., Alja'afreh, M., Abouali, M., Thakur, K., & Karime, A. (2024). Exploring IoT and blockchain: A comprehensive survey on security, integration strategies, applications and future research directions. Big Data and Cognitive Computing, 8(12), 174. https://doi.org/10.3390/bdcc8120174
Kaushik, D., Gulia, P., Gill, N. S., Mohammad, Y., Shukla, P. K., & Shreyas, J. (2026). Synergizing blockchain and AI to fortify IoT security: A comprehensive review. Artificial Intelligence Review, 59(2), Article 41. https://doi.org/10.1007/s10462-025-11434-0
Juber, A. H., & Farhan, B. I. (2026). Innovative strategies for IoT security using AI and blockchain: A comprehensive review. Iraqi Journal for Computers and Informatics, 52(1), 99–112. https://doi.org/10.25195/ijci.v52i1.656
Hassan, J., Abid, M. K., Ahmad, M., Ghulam, A., Fakhar, M. S., & Asif, M. (2023). A survey on blockchain based intrusion detection systems for IoT. VAWKUM Transactions on Computer Sciences, 11(1). https://doi.org/10.21015/vtcs.v11i1.1385
AlE'mari, S., Anbar, M., Sanjalawe, Y., Manickam, S., & Hasbullah, I. (2022). Intrusion detection systems using blockchain technology: A review, issues and challenges. Computer Systems Science and Engineering, 40(1), 87–112. https://doi.org/10.32604/csse.2022.017941
Khraisat, A., Alazab, A., & Alazab, M. (2025). Federated learning for intrusion detection in IoT environments: A privacy-preserving strategy. Discover Internet of Things, 5, 72. https://doi.org/10.1007/s43926-025-00169-7
Pagano, A., Ferraro, G., & Petralia, G. (2025). Federated learning-driven cybersecurity framework for IoT networks with privacy preserving and real-time threat detection capabilities. Informatics, 12(3), 62. https://doi.org/10.3390/informatics12030062
Al-Sarem, M., Al-Hagery, M., & Saeed, F. (2025). A survey on cybersecurity in IoT. Future Internet, 17(1), 30. https://doi.org/10.3390/fi17010030
Rivadeneira, J. E., Díaz-Verdejo, J. E., García-Teodoro, P., & González-Manzano, L. (2025). A literature review on security in the Internet of Things: Identifying and analysing critical categories. Computers, 14(2), 61. https://doi.org/10.3390/computers14020061
Tuptuk, N., Hazell, P., Watson, J., & Hailes, S. (2023). A survey on cyber risk management for the Internet of Things. Applied Sciences, 13(15), 9032. https://doi.org/10.3390/app13159032
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