Bayesian Spam Detection Framework on Mobile Device

Background: The spam message aimed at mobile devices has become a prevalent activity in recent years. About 76% of mobile users received spam messages of Ads, fraud messages and phishing links. It has led to a significant financial loss of 91.5 billion Yuan per year and this problem will be more severe in the coming future. Although the operators are making much effort against spam message, the number of spams are increasing day by day. Aim: Many schemes designed to detect and filter spam have been proposed in the research area. The Bayesian classification algorithm is one of the most popular schemes. Most of the spam detection schemes based on Bayesian are designed for communication providers. The scalability of filter policy is hard to control because the strict policy might filter the normal messages while some spam messages would not be detected due to the flexibility in the policy. Methods: The current mobile device has a robust computational capability so it can execute complex jobs. A spam detection framework is designed in the present study. Results: It can sniffer the coming messages in mobile device by hooking the Android SMS API, and send them to the filter module, -which is responsible to filter messages into normal or spam based on Bayesian classification. This is a light-weight framework of consuming low power, which is suitable for mobile devices. Extra experiments are conducted to prove their accuracy and efficiency. Conclusion: The results showed that it could filter the spam among receiving the messages in realtime and become more accurate by learning the users' feedback.