Carnegie Mellon University
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New Streaming Algorithms for Fast Detection of Superspreaders

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journal contribution
posted on 2005-01-01, 00:00 authored by Shoba Venkataraman, Dawn Song, Phillip B Gibbons, Avrim Blum
High-speed monitoring of Internet traffic is an important and challenging problem, with applications to realtime attack detection and mitigation, traffic engineering, etc. However, packet-level monitoring requires fast streaming algorithms that use very little memory and little communication among collaborating network monitoring points. In this paper, we consider the problem of detecting superspreaders, which are sources that connect to a large number of distinct destinations. We propose new streaming algorithms for detecting superspreaders and prove guarantees on their accuracy and memory requirements. We also show experimental results on real network traces. Our algorithms are substantially more efficient (both theoretically and experimentally) than previous approaches. We also extend our algorithms to identify superspreaders in a distributed setting, with sliding windows, and when deletions are allowed in the stream (which lets us identify sources that make a large number of failed connections to distinct destinations). More generally, our algorithms are applicable to any problem that can be formulated as follows: given a stream of (x; y) pairs, find all the x’s that are paired with a large number of distinct y’s. We call this the heavy distinct-hitters problem. There are many network security applications of this general problem. This paper discusses these applications and, for concreteness, focuses on the superspreader problem.




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