SMS Spam Detection Using Noncontent Features

Short Message Service text messages are indispensable, but they face a serious problem from spamming. This service-side solution uses graph data mining to distinguish spammers from nonspammers and detect spam without checking a message's contents.

Authors

• Qian Xu HKUST
• Evan Wei Xiang
• Qiang Yang Hong Kong University of Science and Technology, Hong Kong SAR, China
• Jiachun Du
• Jieping Zhong

Keywords

• Support vector machines
• Feature extraction
• Classification algorithms
• Electronic mail
• Telecommunications
• Short message services
• Unsolicited electronic mail
• Data Mining
• Social Networking Sites
• Short Message
• Millions Of People Worldwide
• Telecom Operators
• Short Text Messages
• Support Vector Machine
• Logarithmic Scale
• Network Characteristics
• Temporal Features
• Temporal Information
• Support Vector Machine Classifier
• Mobile Communication
• Clustering Coefficient
• Quadratic Programming
• Training Examples
• Information Messages
• Number Of Recipients
• Legitimate Users
• Normal Users
• Message Size
• Spam Emails
• Short Message Service
• spam detection
• SMS spam
• social media spam