RESEARCH ON THE EFFECTIVENESS OF DEVELOPED ALGORITHMS AND PROTECTION MODELS IN INTERNET OF THINGS NETWORKS
DOI:
https://doi.org/10.17721/2519-481X/2025/88-09Keywords:
internet of Things, traffic modeling, cybersecurity, protection algorithms, 5G, IDS, anomaliesAbstract
The article considers approaches to ensuring high-quality service of Internet of Things (IoT) services in 5G networks, taking into account modern challenges in terms of scalability, variable topology and security threats. Considerable attention is paid to modeling IoT traffic, building mathematical service models and assessing parameters that affect the level of reliability and timeliness of data delivery.
During the study, the specifics of the structure of IoT networks were analyzed, key traffic metrics were identified and a mathematical model was built based on the classical Poisson model, as well as its extensions MMPP and BMAP to adequately reflect the variable and clustered traffic structure in IoT networks. Clustering was performed using the k-means algorithm to profile normal node behavior, which made it possible to detect anomalies in real time. A formal risk assessment model was proposed that takes into account deviations of current metrics from a typical cluster and changes in intensity. An adaptive incident response mechanism with threshold values that determine the level of intervention from limiting activity to completely blocking the device. For verification, DDoS, device substitution, and stealth attack scenarios were simulated. The effectiveness of the detection algorithm was assessed using the metrics precision, recall, F1-score, and response time.
The results showed that the proposed methodology provides high anomaly detection accuracy (F1-score = 92.5%) with a response time of up to 80 ms, which makes it suitable for use in real IoT systems. The proposed approach allows for increased network reliability and security without significantly increasing computational costs. The findings confirm the feasibility of implementing intelligent monitoring systems based on clustering and risk models in the 5G-IoT infrastructure.
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