Big-DAMA: 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. We are witnessing a major development in Big Data Analysis Frameworks (BDAFs), but the application of BDAFs and scalable analysis techniques to the NTMA domain remains poorly understood and only in-house and difficult to benchmark solutions are conceived. In this position paper we describe the basis of the Big-DAMA research project, which aims at tackling this growing need by benchmarking and developing novel scalable techniques and frameworks capable to analyze both online network traffic data streams and offline massive traffic datasets.