Statistical and machine learning approaches for detecting anomalies in large-volume network traffic
Keywords:
Network traffic analysis, anomaly detection, big data, statistical approaches, supervised learning, unsupervised learning, hybrid model, IoT security, cloud computing, 5G networks, real-time monitoring, post-quantum cryptography, autoencoderAbstract
Detecting anomalies in large-scale network traffic is one of the pressing issues in modern information security. The volume of traffic generated as a result of the expansion of Internet services, cloud computing, the development of IoT and 5G networks is increasing dramatically, and this process reduces the effectiveness of traditional security mechanisms. This article studies and compares statistical methods and machine learning approaches to detect anomalous behavior in the network. The advantages of statistical approaches, analysis of variance and time series models, are explained by their performance and efficiency in real-time monitoring, but their accuracy is limited in large-scale data.
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