Anti-Spam Filtering Using Neural Networks and Baysian Classifiers

Abstract

Electronic mail is inarguably the most widely used Internet technology today. With the massive amount of information and speed the Internet is able to handle, communication has been revolutionized with email and other online communication systems. However, some computer users have abused the technology used to drive these communications, by sending out thousands and thousands of spam emails with little or no purpose other than to increase traffic or decrease bandwidth. This paper evaluates the effectiveness of email classifiers based on the feedforward backpropagation neural network and Baysian classifiers. Results are evaluated using accuracy and sensitivity metrics. The results show that the feedforward backpropagation network algorithm classifier provides relatively high accuracy and sensitivity that makes it competitive to the best known classifiers. On the other hand, though Baysian classifiers are not as accurate they are very easy to construct and can easily adapt to changes in spam patterns.