Detection of IoT Devices and Network Anomalies based on Anonymized Network Traffic

Abstract

Nowadays, a crucial aspect of IoT networks management is traffic monitoring, where Machine Learning (ML) arose as a tool to perform several tasks, such as IoT device identification and network anomalies detection. However, the access to information about network traffic can affect users’ privacy, regarding devices and users identification and, consequently, violating existing privacy regulations. Thus, a solution to perform anomaly detection without compromising privacy is necessary. Within this context, this paper presents an ML solution to perform network anomaly detection based on anonymized network traffic, ensuring privacy when identifying the device and performing features selection. Experiments using a real IoT network traffic dataset indicate that it is possible to detect network anomalies with 99% accuracy while preserving privacy.