Funded in 2022 –

A grant to support “Advanced Deep Learning-based Analysis of Network Traffic Generated by Communication-and-Collaboration Apps used in COVID-19 pandemic” (ADDITIONAL) was awarded in 2022 to the CINI  research unit at the University of Napoli Federico II, Italy. The project page is available at

The project aims at applying advanced Deep Learning methods to support two critical tasks in managing modern networks: 1) traffic classification (i.e., associating observed traffic with the application that generated it) and 2) fine-grained prediction (i.e., forecasting traffic behavior, knowing the kind of application generating it).”

The COVID-19 pandemic increased the usage of communication-and-collaboration apps (Discord, GotoMeeting, Meet, Messenger, Skype, Slack, Teams, Webex, and Zoom) by mobile users. This sudden shift in Internet traffic poses a challenge to efficient network management and calls for enhanced network analytics tools able to overcome traditional monitoring techniques requiring time-consuming human experts’ intervention to adapt to the new conditions. Accordingly, ADDITIONAL aims at designing, implementing, and evaluating innovative tools based on Artificial Intelligence methodologies, with particular reference to Deep Learning architectures, to support network traffic classification and prediction. Given the heterogeneous and large-scale landscape of mobile apps, the focus is on gathering fine-grained network visibility. The proposal matches the interests of the scientific community, being beneficial to network providers, customers, and researchers.

The ADDITIONAL project duration is five months. Works include (i) the collection and annotation of the dataset, then (ii) a systematic study of the classification and prediction performance of state-of-the-art approaches, and finally (iii) the research and experimental validation of novel and improved classification and prediction approaches.

The ADDITIONAL proposal was relayed to the Vietsch Foundation by GÉANT through its Innovation Programme