ANALYSIS OF METHODS FOR DETECTING ANOMALOUS TRAFFIC IN IOT NETWORKS
DOI:
https://doi.org/10.26906/SUNZ.2024.1.133Keywords:
Internet of Things, anomalous traffic, machine learning, deep learning, statistical analysisAbstract
The aim of this work is to conduct a comprehensive analysis of methods and approaches for anomaly detection in Internet of Things (IoT) networks. Considering the rapid development of IoT and the increasing number of connected devices, the problem of detecting anomalous traffic becomes crucial for ensuring the security and efficiency of these networks. This study examines various methods and approaches to anomaly detection, including statistical analysis, network monitoring, behavioral analysis, as well as the application of modern machine learning and deep learning technologies. Each of these methods is considered from the perspective of its applicability in the context of IoT and its advantages and limitations are evaluated. The work also explores current challenges and future prospects in the field of IoT security, with a focus on protection against cyber threats and the enhancement of anomaly detection systems.Downloads
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