Adaptive Network Security through Stream Machine Learning

Stream Machine Learning is rapidly gaining popularity within the network monitoring community as the big data produced by network devices and end-user terminals goes beyond the memory constraints of standard monitoring equipment. We consider a stream-based machine learning approach to network security, conceiving adaptive machine learning algorithms for the analysis of continuously evolving network data streams. Using a sliding-windowadaptive-size approach, we show that adaptive random forests models are able to keep up with important concept drifts in the underlying network data streams, by keeping high accuracy with continuous re-training at concept drift detection times.