BLINC: Multilevel Traffic Classification in the Dark - Karagiannis, Thomas; Papagiannaki, Konstantina; Faloutsos, Michalis (2005)
We present a fundamentally different approach to classifying trafﬁc ﬂows according to the applications that generate them. In contrast to previous methods, our approach is based on observing and identifying patterns of host behavior at the transport layer. We analyze these patterns at three levels of increasing detail (i) the social, (ii) the functional and (iii) the application level. This multilevel approach of looking at trafﬁc ﬂow is probably the most important contribution of this paper. Furthermore, our approach has two important features. First, it operates in the dark, having (a) no access to packet payload, (b) no knowledge of port numbers and (c) no additional information other than what current ﬂow collectors provide. These restrictions respect privacy, technological and practical constraints. Second, it can be tuned to balance the accuracy of the classiﬁcation versus the number of successfully classiﬁed trafﬁc ﬂows. We demonstrate the effectiveness of our approach on three real traces. Our results show that we are able to classify 80%-90% of the trafﬁc with more than 95% accuracy.