Hybrid GA-AIS for Efficient Feature Extraction in E-mail Spam Detection

pam is a serious universal problem which causes problems for almost all computer users. This issue affects not only normal users of the internet, but also causes a big problem for companies and organizations since it costs a huge amount of money in lost productivity, wasting users’ time and network bandwidth. Many studies on spam indicate that spam cost organizations billions of dollars yearly. In this paper, Spam Detection using Combination of GA-AIS Algorithm and Classification using SVM (support vector machine). The proposed approach is based on the characteristics of the spam emails. The spam e-mails are categorized into 5500 features and then the ensemble approach is performed to classify them, also increase of the number of input features, it has the lowest run-time. Also the suggested method has acceptable accuracy for 10000 data compared to the similar methods, and also less computation time and complexity.