A New Model for Email Spam Detection using Hybrid of Magnetic Optimization Algorithm with Harmony Search Algorithm

Unfortunately, among internet services, users are faced with several unwanted messages that are not even related to their interests and scope, and they contain advertising or even malicious content. Spam email contains a huge collection of infected and malicious advertising emails that harms data destroying and stealing personal information for malicious purposes. In most cases, spam emails contain malware that is usually sent to users in the form of scripts or attachments, and the user infects the computer with malware by downloading and executing the attached file. In this paper, a new model for detecting spam e-mail is proposed based on the hybrid of the Harmony Search Algorithm (HAS) with the Magnetic Optimization Algorithm (MOA). The proposed model is used to select the effective features and then the classification is performed using the K Nearest Neighbor's (KNN) algorithm. In the proposed model, using the MOA was found the best features for the HSA, and the harmony matrix is formed based on them. Then the HSA changes based on the update and rate of step-change in each step of the harmony vectors so that the best vector is selected as the vector of characteristics among them. The results show that the accuracy of the proposed model on the Spam base dataset with 200 iterations is 94.17% and also the accuracy of the diagnostic model of the proposed model is more than other models.