Providing an Improved Feature Extraction Method for Spam Detection Based on Genetic Algorithm in an Immune System

he increasing use of e-mail in the world because of its simplicity and low cost, has led many Internet users are interested in developing their work in the context of the Internet. In the meantime, many of the natural or legal persons, to sending e-mails unrelated to mass. Hence, classification and identification of spam emails is very important. Many studies on spam indicate that it costs organizations billions of dollars annually. We introduce a Genetic Algorithm (GA) assisted Artificial Immune System AIS in spam detection, and compare between two methods. Results were tested on 1000 standard datasets of Spam Assassin email. The proposed method may be used in conjunction with other filtering systems to minimize errors and Run time algorithms.