IoT Attack Detection with Deep Learning Analysis
Riccardo Pecori, Amin Tayebi, Armando Vannucci, Luca Veltri
Abstract
Internet traffic detection and classification has been thoroughly studied in the last decade, but this is still a hot topic as regards the Internet of Things (IoT), a communication paradigm that is going to involve different aspects of our daily life. As a consequence, researchers started applying traditional methods for traffic classification also to the traffic flows coming and addressed to smart devices. In this paper, we created a large integrated dataset of IoT traffic flows, coming from four different network scenarios, in order to have a benchmark for future research. Moreover, we used this dataset to test the effectiveness of a deep learning network model, made of different hidden layers, and we compare its outcomes with the ones obtained through traditional machine learning approaches, demonstrating the superiority of our deep learning architecture in both a binary and multinomial classification.