Application of Data Mining in Spam Detection
Lin Dong-mao · 2012
Research spam detection problems.Network security is improved.Mail has the features of high dimension,high redundancy,the traditional testing model cannot reduce the feature dimension and eliminate redundant information,leading to long computation time and space complexity.In order to improve the detection rate of garbage mails,the paper put forward a two layers spam detection model which combined white list with support vector machine.Feature clustering technique was used to reduce cluster feature dimensions and eliminate redundant information.The white list detection technology was used as the first defense line of garbage detection system to detect the spams whose addresses were known.The support vector machine was used as the second defense line to,test new spasm and enhance the network security.The spam data were used to test the model's performance.The experimental results show that the two-layer spam detection model can effectively improve the spam detection efficiency and accuracy,and has certain application value.