Name
Abstract | ||
|---|---|---|
Social network spam increases explosively with the rapid development and wide usage of various social networks on the Internet. To timely detect spam in large social network sites, it is desirable to discover unsupervised schemes that can save the training cost of supervised schemes. In this work, we first show several limitations of existing unsupervised detection schemes. The main reason behind the limitations is that existing schemes heavily rely on spamming patterns that are constantly changing to avoid detection. Motivated by our observations, we first propose a sybil defense based spam detection scheme SD2 that remarkably outperforms existing schemes by taking the social network relationship into consideration. In order to make it highly robust in facing an increased level of spam attacks, we further design an unsupervised spam detection scheme, called UNIK. Instead of detecting spammers directly, UNIK works by deliberately removing non-spammers from the network, leveraging both the social graph and the user-link graph. The underpinning of UNIK is that while spammers constantly change their patterns to evade detection, non-spammers do not have to do so and thus have a relatively non-volatile pattern. UNIK has comparable performance to SD2 when it is applied to a large social network site, and outperforms SD2 significantly when the level of spam attacks increases. Based on detection results of UNIK, we further analyze several identified spam campaigns in this social network site. The result shows that different spammer clusters demonstrate distinct characteristics, implying the volatility of spamming patterns and the ability of UNIK to automatically extract spam signatures. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2505515.2505581 | CIKM |
Keywords | Field | DocType |
unsupervised spam detection scheme,social network spam,spam attack,spam signature,spam attacks increase,spam campaign,unsupervised social network spam,social graph,spam detection scheme,spamming pattern,large social network site,social networks | Data mining,Graph,Social graph,Social network,Computer science,Spambot,Artificial intelligence,Machine learning,Spamming,The Internet | Conference |
Citations | PageRank | References |
31 | 1.08 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enhua Tan | 1 | 815 | 47.39 |
| Lei Guo | 2 | 840 | 55.02 |
| Songqing Chen | 3 | 1380 | 102.76 |
| Xiaodong Zhang | 4 | 5378 | 355.72 |
| Yihong Zhao | 5 | 110 | 39.34 |