What IS IT ABOUT?
Big Data Frameworks
We are using SQL and No-SQL frameworks for Big Data Analytics, including stream- and batch-based networking data processing
We are building and testing novel Machine-Learning based approaches for large-scale network measurements analytics
We are conceiving new benchmarks for comparing the performance of big data stream analysis and big network traffic monitoring solutions
We are developing novel techniques for large-scale network traffic monitoring and analysis, including network security and anomaly detection
Big-DAMA Use cases
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
Félix Iglesias Vázquez
Big-DAMA is proudly gold sponsor for TMA Conference 2018
We are organizing the 2nd edition of the Big-DAMA workshop on Big Data Analytics and Machine Learning for Data Communication Networks @SIGCOMM 2018
Big-DAMA is proudly bronze sponsor for SIGCOMM 2017
We are organizing a new workshop on Big Data Analytics and Machine Learning for Data Communication Networks @SIGCOMM 2017
“Traffic Analysis with Off-the-Shelf Hardware: Challenges and Lessons Learned”
The second plenary meeting will take place @AIT Vienna on the 3th and 4th November 2016.
“Machine Learning based Prediction of Internet Path Dynamics”
“(Semi)-Supervised Machine Learning Approaches for Network Security in High-Dimensional Network Data”
“When Smartphones become the Enemy: Unveiling Mobile Apps Anomalies through Clustering Techniques”
“CLUE: Clustering for Mining Web URLs”
We are presenting 3 papers at the IEEE TRAC workshop.
Big-DAMA was presented to the Viennese research and local authorities at the yearly WWTF science gala dinner.
“Grasping Popular Applications in Cellular Networks with Big Data Analytics Platforms”
“DBStream: a Holistic Approach to Large-scale Network Traffic Monitoring and Analysis”
We are presenting the Big-DAMA project at the ACM SIGCOMM LANCOMM workshop 2016 in August!
“Machine-Learning Based Approaches for Anomaly Detection and Classification in Cellular Networks”