Research on spam message recognition algorithm based on improved naive Bayes
Jihui Fan, Fengshan Yuan · 2022
Research shows that in 2018, there are about 8.4 billion spam messages in China (360 Internet Security Center), which focuses on bank fraud, false online shopping on the Internet, telecommunications fraud, etc. China's current status of spam messages, the black interest chain of spam messages, the lack of legal protection, the increasingly changeable types of SMS, the continuous improvement of delivery methods, the changeable contents of spam messages and the various types of spam messages. Some bad organizations even collect spam messages through various channels Collect personal mobile phone information and sell mobile phone information to businesses and business personnel to obtain illegal benefits. At the same time, businesses seek benefits by sending spam short interest such as fraud and advertising, these methods endangers the information security and broke the daily life of the users. Based on the text content of SMS, this paper establishes a recognition model to accurately identify spam SMS, so as to solve the problem of spam SMS filtering. Using python programming, Gaussian naive Bayes is used to realize text classification, and a high classification accuracy is achieved.