Spam Spoiler for Public and Private E-Mail Services

Abstract

Abstract: In recent years, cyber security incidents have occurred frequently. In most of these incidents, attackers have used different types of spam email as a knock-on to successfully invade government systems, well-known companies, and websites of politicians and social organizations in many countries. The detection of spam mail from big email data has been paid public attention. However, the camouflage technology of spam mail is becoming more and more complex, and the existing detection methods are unable to confront the increasingly complex deception methods and the growing number of emails. In this project, we proposed to design a novel efficient approach named Spam Spoiler for big email data classification into four different classes: Normal, Fraudulent, Harassment, and Suspicious E-mails by using LSTM-based GRU. The new method includes two important stages, the sample expansion stage and the testing stage under sufficient samples. This project the LSTM-based GRU efficiently captures meaningful information from E-mails that can be used for forensic analysis as evidence. Experimental results revealed that Spam Spoiler performed better than existing ML algorithms and achieved a classification accuracy of 98% using the novel technique of LSTM with recurrent gradient units. As different types of topics are discussed in E-mail content analysis. Spam Spoiler effectively outperforms existing methods while keeping the classification process robust and reliable.