Digital spectral analysis: with applications
Digital spectral analysis: with applications
C4.5: programs for machine learning
C4.5: programs for machine learning
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Content based SMS spam filtering
Proceedings of the 2006 ACM symposium on Document engineering
Feature engineering for mobile (SMS) spam filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Spam filtering for short messages
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Detecting splogs via temporal dynamics using self-similarity analysis
ACM Transactions on the Web (TWEB)
Filtering Short Message Spam of Group Sending Using CAPTCHA
WKDD '08 Proceedings of the First International Workshop on Knowledge Discovery and Data Mining
Querying graph databases
The contribution of stylistic information to content-based mobile spam filtering
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Detecting spammers with SNARE: spatio-temporal network-level automatic reputation engine
SSYM'09 Proceedings of the 18th conference on USENIX security symposium
Simple SMS spam filtering on independent mobile phone
Security and Communication Networks
Crime scene investigation: SMS spam data analysis
Proceedings of the 2012 ACM conference on Internet measurement conference
Discovery of emergent malicious campaigns in cellular networks
Proceedings of the 29th Annual Computer Security Applications Conference
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Short messaging service (SMS) is one of the fastest-growing telecom value-added services worldwide. However, mobile message spam is a side effect for ordinary mobile phone users that seriously troubles their daily life and, as a result, threatens the revenue of telecom operators. In this paper, we present an SMS antispam system that combines behavior-based social network and temporal (spectral) analysis to detect spammers with both high precision and recall. The system infrastructure and the proposed approximate neighborhood index solution, which solves the scalability issue of social networks, are described in detail. Experimental results demonstrate that our proposed system achieves excellent discrimination between spammers and legitimates, and even with fixed recall at 95%, the online system and offline detection subsystems maintain a precision of about 98% and 99.5%, respectively.