An Efficient Model for Smartphone Forensics Using SMS Spam Filtering

With the development of smartphones and mobile communication technology, short message service (SMS) has been becoming more and more popular based on the low cost and easy operation. Research data show that more than 95% of mobile users will read their SMS at the end of the day, but about 80 percent of emails are ignored. In practice, SMS might be misused by unscrupulous people, for example, some illegal businessman or companies will employ SMS sending lots of advertising information to make a profit. In the work of smartphone forensics, if various spam messages are stored at people's smartphones, investigators will spend much time and human effort to delete spam. To perform investigation in smartphone efficiently, in this paper, we present a smartphone forensics model that based on machine learning technique to filter SMS spam and segregate the relevance evidence for investigation.