BIG-DAMA
BIG DATA ANALYTICS FOR NETWORK TRAFFIC MONITORING AND ANALYSIS

What IS IT ABOUT?

The complexity of the Internet has dramatically increased in the last few years, making it more important and challenging to design scalable Network Traffic Monitoring and Analysis applications and tools. The Big-DAMA project is conceiving novel scalable techniques and big-data frameworks capable to analyze both online network traffic data streams and offline massive traffic datasets. The team is exploring scalable online and offline data mining and machine learning-based techniques to monitor and characterize extremely large network traffic datasets.

Big Data Frameworks

We are using SQL and No-SQL frameworks for Big Data Analytics, including stream- and batch-based networking data processing

Machine Learning

We are building and testing novel Machine-Learning based approaches for large-scale network measurements analytics

Benchmarking

We are conceiving new benchmarks for comparing the performance of big data stream analysis and big network traffic monitoring solutions

Network Monitoring

We are developing novel techniques for large-scale network traffic monitoring and analysis, including network security and anomaly detection

PROJECT PARTNERS

The Big-DAMA project is funded by the Vienna Science and Technology Fund (WWTF) through project ICT15-129, “BigDAMA”, as well as by the other partner institutions (Austrian Institute of Technology, Technische Universität Wien and Politecnico di Torino).

              

Big-DAMA Use cases

Network Security and Anomaly Detection are our main applications.

Cyber attackers are continuously looking for new ways to access valuable information and disrupt services. Big-DAMA builds and evaluates specific Big Data Analytics frameworks and techniques for the purpose of detection and characterization of network attacks, considering both on-line data analysis, as well as the extraction of relevant patterns for data forensics analysis.

Despite the long literature and assorted list of proposed systems for performing automatic detection and diagnosis of anomalies in large-scale operational networks, Internet Service Providers (ISPs) are still looking for a Holy Grail which might effectively detect and diagnose the ever-growing number of network traffic anomalies they face in their daily business. Big-DAMA is conceiving a novel framework for detection and diagnosis of network traffic anomalies.

  • Detection of 0-day attacks, based on unsupervised analysis techniques

  • On-line detection and classification of network and service anomalies

  • Internet performance evaluation through large-scale measurements analysis

  • Using machine learning techniques to monitor networks from a user-centric perspective

Big-DAMA Team

The Big-DAMA team consists of a mix of young and confirmed researchers in the domain of Network Traffic Monitoring and Analysis

Pedro Casas

Principal Investigator
Team Leader
Pedro (Ph.D 2010) is Scientist in the Digital Safety & Security department of AIT. His research areas span the monitoring and analysis of network traffic, network security and anomaly detection, QoE assessment, as well as machine-learning based approaches for Networking.

Alessandro D’Alconzo

Senior Investigator
Alessandro (Ph.D 2007) is Scientist in the Digital Safety & Security department of AIT. He is expert in the network measurements and traffic monitoring area, in particular in the design and implementation of statistical based anomaly detection and automatic diagnosis solutions.

Tanja Zseby

Co-Principal Investigator
Tanja (Ph.D. 2005) is professor of communication networks at the faculty of electrical engineering and information technology at the Vienna University of Technology. She works in the areas of Network Security, Anomaly Detection and Smart Grid Security.

Marco Mellia

Senior Investigator
Marco (Ph.D 2001) is Associate Professor at the Electronics and Telecommunications department of Politecnico di Torino. He is a highly recognized researcher in the area of network traffic monitoring and analysis, and he is leading the BigData@Polito initiative on big data analysis.

Andrea Morichetta

Junior Investigator
Andrea is Ph.D. candidate at the Electronics and Telecommunications department of Politecnico di Torino. He is working on network security and anomaly detection using clustering techniques and big data platforms.

Félix Iglesias Vázquez

Senior Investigator
Félix (Ph.D 2012) is Postdoc University Assistant at the Vienna University of Technology. His research topics include machine learning, data analysis and network security.

Idilio Drago

Senior Investigator
Idilio (Ph.D 2013) is Assistant Professor at the Telecommunication Networks Group in Politecnico di Torino. His research interests include Internet measurements, Big Data analysis, and network security.

Stefano Traverso

Senior Investigator
Stefano is a researcher at the Telecommunication Networks Group in Politecnico di Torino. His research interests include Internet measurements and network security.

Daniel Ferreira

Junior Investigator
Daniel is Ph.D. candidate at the faculty of electrical engineering and information technology at the Vienna University of Technology. He is working in the area of Network Security, Anomaly Detection and Big Data.

Juan Vanerio

Junior Investigator
Juan is Electrical Engineer and Master of Science candidate at the Electrical Engineering institute of the University of Uruguay (UdelaR). He is working on machine learning tools and techniques for network security and anomaly detection using big data platforms.

Sarah Wassermann

Junior Investigator
Sarah is Master of Science candidate at the Université de Liège. She is working on machine learning modeling and prediction of Internet path dynamics. Her research areas span the monitoring and analysis of network traffic, distributed Internet measurements, as well as machine-learning.

Francesca Soro

Junior Investigator
Francesca is Master of Science candidate at Politecnico di Torino. She will join the Big-DAMA team to develop Big Data analytics and Machine Learning algorithms on the Big-DAMA platform.

International Collaboration Institutions

Big-DAMA has active collaboration with 3 top-level international research institutions

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