A Neural Network Classifier for Junk E-Mail

Abstract. Most e-mail readers spend a non-trivial amount of time regularly deleting junk e-mail (spam) messages, even as an expanding volume of such e-mail occupies server storage space and consumes network bandwidth. An ongoing challenge, therefore, rests within the development and refinement of automatic classifiers that can distinguish legitimate e-mail from spam. A few published studies have examined spam detectors using Naïve Bayesian approaches and large feature sets of binary attributes that determine the existence of common keywords in spam, and many commercial applications also use Naïve Bayesian techniques. Spammers recognize these attempts to thwart their messages and have developed tactics to circumvent these filters, but these evasive tactics are themselves patterns that human readers can often identify quickly. This preliminary study tests an alternative approach using a neural network (NN) classifier on a corpus of e-mail messages from one user. The feature set uses de...

• Type: Conference
• Year: 2004
• Where: DAS
• Authors: Ian Stuart, Sung-Hyuk Cha, Charles C. Tappert
• Keywords: DAS 2004, Document Analysis, E-mail, Naïve Bayesian Approaches, Spam Detectors