Anomaly Detection of actual IoT traffic flows through Deep Learning

The detection and classification of Internet traffic was studied in depth in the last twenty years, but this is still an open research issue as pertains the Internet of Things (IoT), mainly because real IoT traffic dataset are not very widespread. With this paper, we make public an integrated dataset, made of actual IoT network flows, built using six different network sources, which could represent a research reference for further investigations. Furthermore, we exploited it to optimize the hyper-parameters of a deep neural network and evaluate its performance for both distinguishing normal and abnormal traffic and discriminating different types of attacks, achieving very good results.