Tracking Long Duration Flows in Network Traffic

We propose the tracking of long duration flows as a new network measurement primitive. Long-duration flows are characterized by their long lived nature in time, and may not have high traffic volumes. We propose an efficient data streaming algorithm to effectively track long duration flows. Our basic technique is to maintain only two Bloom filters at any given time. In each time duration, only old flows that appear in the current time duration get copied to the current Bloom filter. Our basic algorithm is further enhanced by sampling. Using real network traces, we show that our tracking algorithm is very accurate with low false positive and false negative probabilities. Using multi-faceted analysis, we show that more than 50\% of hosts participating in long duration flows (duration no less than 30 minutes) are blacklisted by various public sources.