Impacto da Anonimização do Tráfego em Redes na Identificação de Dispositivos e na Detecção de Anomalias
Abstract
Currently, a crucial aspect of networking management is monitoring it’s traffic, where Machine Learning (ML) comes in. ML has been used to perform various tasks, such as loT device identification and detection of network anomalies. However, access to information about network traffic can affect users’ privacy, thus violating existing privacy laws. Within this context, this article analyzes the impact of network traffic anonymization, ensuring privacy when identifying the device and detecting anomaly solutions by basing on feature selection techniques. The carried out experiments, used a real dataset, which results showed that when using the selection and ML techniques combined, the anonymization of traffic reduces the identification capacity. In addition, using those techniques also preserve user’s privacy while maintaining the detection capacity of network anomalies.
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