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CatchSync: Catching Synchronized Behavior in Large Directed Graphs (2014)
| Citations: | 3 - 2 self |
BibTeX
@MISC{Jiang14catchsync:catching,
author = {Meng Jiang and Peng Cui and Alex Beutel and Christos Faloutsos and Shiqiang Yang},
title = {CatchSync: Catching Synchronized Behavior in Large Directed Graphs},
year = {2014}
}
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Abstract
Given a directed graph of millions of nodes, how can we automatically spot anomalous, suspicious nodes, judging only from their connectivity patterns? Suspicious graph patterns show up in many applications, from Twitter users who buy fake followers, manipulating the social network, to botnet members performing distributed denial of service attacks, disturbing the network traffic graph. We propose a fast and effective method, CATCHSYNC, which exploits two of the tell-tale signs left in graphs by fraudsters: (a) synchronized behavior: suspicious nodes have extremely similar behavior pattern, because they are often required to perform some task together (such as follow the same user); and (b) rare behavior: their connectivity patterns are very different from the majority. We introduce novel measures to quantify both concepts (“synchronicity ” and “normality”) and we propose a parameter-free al-
Keyphrases
catching synchronized behavior large directed graph suspicious node connectivity pattern twitter user network traf-fic graph tell-tale sign distributed denial many application similar behavior pattern directed graph suspicious graph pattern social network fake follower novel measure service attack parameter-free al effective method
