Big Data Analytics for network traffic Monitoring and Analysis

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 (NTMA) applications and tools. Critical NTMA applications such as the detection of anomalies, network attacks and intrusions, require fast mechanisms for online analysis of thousands of events per second, as well as efficient techniques for offline analysis of massive historical data.

The Big-DAMA project conceived novel scalable techniques and big-data frameworks capable to analyze both online network traffic data streams and offline massive traffic datasets. Big-DAMA explored scalable online and offline data mining and machine learning-based techniques to monitor and characterize extremely large network traffic datasets. The project pushed forward big-data analysis technologies by building novel frameworks upon the most suitable tools for online and offline networking data processing, following the lessons learned from DBStream, our promising work in this direction, available as open software for the community. Big-DAMA also provided a new benchmark for big data stream analysis, enabling the quantitative and qualitative comparison of available and future Big Data Analysis Frameworks (BDAFs) for NTMA.

Facts:

• Projektbeginn: March 2016
• Projektdauer: 36 Monate
• Budget: ca. 650k EUR
• Förderung: WWTF

Webpage: https://bigdama.ait.ac.at/