Semantic Graph Based Convolutional Neural Network for Spam e-mail Classification in Cybercrime Applications

Abstract

Spam is characterized as unnecessary and garbage E-mails. Due to the increasing of unsolicited E-mails, it is becoming more and more crucial for mail users to utilize a trustworthy spam E-mail filter. The shortcomings of spam classifier are defined by their increasing inability to manage large amounts of relevant messages and to effectively detect and effectively detect spam messages. Numerous characteristics in spam classifications are problematic. Given that selecting features is one of the most often used and successful techniques for feature reduction, it is a crucial duty in the identification of keyword content. As a result, features that are unnecessary and pointless yet potentially harm effciency would be removed. In this study, we present SGNNCNN (Semantic Graph Neural Network With CNN) as a solution to tackle the diffcult task of mail identification. By projections E-mails onto a graph and by using the SGNN-CNN model for classifications, this technique transforms the E-mail classification issue into a graph classification challenge. There is no need to integrate the word into a representation since the E-mail characteristics are produced from the semantic network. On several open databases, the technique's effectiveness is evaluated. Some few public databases were used in experiments to demonstrate the high accuracy of the proposed approach for classifying E-mails. In term of spam classification, the performance is superior to state-of-the-art deep learning-based methods.