The ADDITIONAL project aims to support the analysis of network traffic generated by communication and collaboration apps exploiting advanced Deep Learning approaches. The project is funded by Vietsch Foundation to the CINI research unit at the University of Napoli Federico II, Italy.

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. Indeed, after the COVID-19 pandemic, these activities have become even more crucial, due to the sudden widespread use of communication-and-collaboration mobile apps (video-conference, chat, document sharing) which has changed the shape of network traffic. The resulting knowledge is instrumental for advanced monitoring and for optimizing the management of digital network infrastructures (e.g., implementation of QoS and security policies) via privacy-preserving tools. The expected outcomes match the interests of both the scientific and industrial communities, being beneficial to network providers, customers and researchers.

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MIRAGE-COVID-CCMA-MA-2023 takes into consideration the traffic generated by more than 50 experimenters using 14 mobile apps for communication and collaboration via 2 devices. The experimenters used each app to perform at most 4 different user activities. In this new dataset, we analyzed the traffic when the user performs multiple activities (MA) during a single session with a given app (i.e., multiple activities in a single traffic capture).
The dataset will soon be available for downloading.


MIRAGE-COVID-CCMA-2022 takes into consideration the traffic generated by more than 150 experimenters using 9 mobile apps for communication and collaboration via 3 devices. The experimenters used each app to perform at most 3 different user activities. The dataset is released in two formats, making available both the raw traffic data captured (in JSON format) and a pre-processed version providing the set of inputs (in pickle format) leveraged in our work.

Creative Commons License
MIRAGE-COVID-CCMA-2022 and MIRAGE-COVID-CCMA-MA-2023 datasets are released under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

CrowdSourced Collection

Photo by Windows on Unsplash

We collected our datasets by utilizing the Mirage architecture, which was conveniently optimized for capturing the traffic generated by communication and collaboration apps. The collection took place in the ARCLAB laboratory at the University of Napoli "Federico II".

Four different mobile devices were used for the experiments, all running on Android 10: a Google Nexus 6, a Xiaomi Mi 10 Lite, and two Samsung Galaxy A5.

The collection campaigns involved more than 200 volunteer students, both undergraduate and graduated, and researchers as experimenters who took on the role of typical users of the apps under consideration.

During each capture session, the experimenters engaged in a particular activity on a selected communication and collaboration app. This approach allowed us to obtain a traffic dataset that reflects the typical usage patterns of these apps. The duration of each session varied from 15 to 80 minutes, depending on the specific activity being performed.

Consequently, each session produced a PCAP traffic trace, accompanied by relevant ground-truth information extracted from additional system log-files. Using established network connections through the standard Linux command "netstat," the capture system reliably labeled each biflow with the corresponding Android package name. Furthermore, we enriched this information by adding a custom label that described the specific activity carried out by the user operating the device.

Mobile Apps
Clash Royale

User activities


Methodology and Implementation

This section describes the methodology and implementation aspects of traffic classification and prediction for communication-and-collaboration apps via Deep Learning approaches. Further details can be obtained by clicking on "More Details".

Internet Traffic Classification - More Details

Internet Traffic Prediction - More Details



University Courses

Computer Networks

The topics of interest and objectives of the ADDITIONAL project are presented during the Computing Networks course, chaired by Prof. Antonio Pescapè at the University of Napoli Federico II for the Bachelor's Degree in Computer Engineering.

The Computing Networks course aims to provide the first theoretical notions and the necessary operational skills on computer networks and in general on packet switching communication networks. The course, following a top-down approach, favors an application vision of modern telematic technologies in the first place, to then arrive at the presentation of the software and hardware technologies at the basis of the creation of telematic systems.

During the Computing Networks course, the students are actively involved in the ADDITIONAL project, specifically in the collection campaign of the traffic generated by the communication-and-collaboration apps under study and its early characterization. The students are asked to carry out typical usage of these apps and perform specific activies to properly label collected traffic with both the app the activity tested.

Internet Data Analysis

The topics of interest and objectives of the ADDITIONAL project are presented during the course of Internet Data Analysis, chaired by Prof. Antonio Pescapè at the University of Napoli Federico II for the Master's Degree in Computer Engineering.

The Internet Data Analysis course aims to provide students with the specialized knowledge useful in analyzing a modern Internet network with special reference to network management and security aspects. The course presents the content by adopting an engineering and empirical approach and blends theoretical lectures, practical classes, seminars and exercises. It presents in depth the main aspects and motivations behind the analysis and performance evaluation of a network and then delves into the methodological and practical aspects related to network analysis with a specific focus on the analysis, identification and classification of anomalous events such as, for example, cyber attacks. The course also includes an exercise part functional to the development of a paper.

During the Internet Data Analysis course, the students are actively involved in the ADDITIONAL project, specifically regarding the analysis of the communication-and-collaboration apps' traffic via data-driven approaches, the production of technical reports, and the presentation of results to their colleagues.

DIGITA Academy

DIGITA Website

DIGITA is the "Digital Transformation & Industry Innovation Academy" of the University of Napoli Federico II in partnership with Deloitte Digital. It was created with the aim of providing young talents, with at least a Bachelor's degree, with the necessary skills to bridge the gap between companies and the Digital ecosystem, enabling them to be protagonists in digital transformation. The educational project consists of 9 months, 6 months of classroom lessons and 3 months of Project Work at partner companies.

The students and partner companies of the DIGITA were briefed on the subject matters and objectives of the ADDITIONAL project to foster its dissemination among both students with heterogeneous backgrounds and industry.

International Workshops and Conferences

Learn more about us

TRAFFIC is a research group of the DIETI Department at University of Napoli Federico II, working in the area of computer networks and multimedia, with focus on Network Monitoring and Measurements. Its research involves measuring and evaluating both experimental and operational systems/networks to derive knowledge and models for network behaviors.

TRAFFIC (and this is the main motivation of its name) focuses on monitoring Internet traffic to provide input for developing new algorithms, protocols, and systems for the current and future Internet, with a specific focus on network management and security.

Besides teaching activities, the members of Traffic are involved in several national and international research projects. They have a significant track record with respect to funding (approx. more than 5 Mln euro) and publications (e.g. conferences like SIGCOMM, Conext, IMC and journals like JSAC, IEEE Networks, IEEE Communication Magazines, etc).

We are part of the larger Computer Networks group (COMICS) at University of Napoli Federico II.

Antonio Pescapè

PhD, Full Professor (Principal Investigator)

Giuseppe Aceto

PhD, Associate Professor

Domenico Ciuonzo

PhD, Tenured Assistant Professor

Valerio Persico

PhD, Tenured Assistant Professor

Antonio Montieri

PhD, Assistant Professor

Giampaolo Bovenzi

PhD, Assistant Professor

Idio Guarino


Alfredo Nascita

PhD Student

Ciro Guida

PhD Student


Contact Us


Via Claudio, 21, 80125 Napoli NA, Italia