Design and Implement a Rule-Based Spam Filtering System Using Neural Network

The rule-based spam filtering technology has been a mainstream to filter spam. But the filer rules are static, and can not identify the new spam characteristics. This paper proposes a method to optimize spam filtering rules using neural network and describes the design and implementation of an anti-spam system using the optimized rules. Our system can automatically extract and learn the features of the mails and make dynamic adjustments to static rules. We compare the performance of our system with a famous rule-based spam filter-Spam Assassin and it is shown that our system has a better filtering performance.