E-mail Fraud Detection

Abstract

Spam issues have become worse on social media platforms and apps with the growth of IoT. To solve the problem, researchers have suggested several spam detection techniques. Spam rates are still high despite the use of anti-spam technologies and tactics, especially given the ubiquity of rogue e-mails that lead to dangerous websites. By using up memory or storage space, spam e-mails may cause servers to run slowly. One of the most essential methods for identifying and eliminating spam is filtering e-mails. To this end, various deep learning and machine learning technologies have been used, including Naive Bayes, decision trees, SVM, and random forest. E-mail and Internet of Things spam filters use various machine learning approaches and systems are categorized in this research. Additionally, as more people use mobile devices and SMS services become more affordable, the issue of spam SMS messages is spreading worldwide. This study suggests using a variety of machine learning approaches to detect and get rid of spam as a solution to this problem. According to the trial findings, the TF-IDF with Random Forest classification algorithm outperformed the other examined algorithms in accuracy %. It is only possible to gauge performance on accuracy since the dataset is imbalanced. Therefore, the algorithms must have good precision, recall, and F-measure.