Profiling and Predicting User Activity on a Home Network

This paper reports a study on the characterization of in-home Internet activity behavior based on application usage logs. We collected online activity data from 86 Belgium households for 60 days. We analyzed the activity traces to gain insights on the temporal traffic distribution, interaction regularity, and activity correlations. This analysis is then used to develop a generic method to segment households into designated groups showing similar behavioral profiles. Our technique combines interaction frequencies and regularities across activities for segmentation, and is able to reveal interesting time-slotted profile for each segment. These profiles aim to show the strength of routine behaviors in Internet usage, based on which we present a novel algorithm to predict future Internet activities of a household. Our algorithm shows that 60% of the households online activities can be predicted accurately 70% of times.