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Robust Mobile Spamming Detection Via Graph Patterns
Yuhang Zhao; Zhaoxiang Zhang; Yunhong Wang
2012-11-11
Conference NameInternational Conference on Pattern Recognition
Source PublicationICPR 2012
Conference Date11-15 November 2012
Conference PlaceTsukuba, Japan
AbstractShort message service (SMS) is now an indispensable way of social communication. However the mobile spam is getting increasingly serious, troubling users' daily life and ruining the service quality. We propose a novel approach for spam message detection based on mining the underlying social network of SMS activities. Comparing with strategies on keywords or flow detection, our network-based approach is more robust and difficult to defeat by human spammers. Various levels of features are employed to describe multiple aspects of the network, such as static structures, node activities and evolving situations. Experimental results on real dataset illustrate effectiveness of various features, showing our promising results.
KeywordFeature Extraction Social Network Services Robustness Mobile Communication Unsolicited Electronic Mail Humans Receivers
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13262
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Yuhang Zhao,Zhaoxiang Zhang,Yunhong Wang. Robust Mobile Spamming Detection Via Graph Patterns[C],2012.
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