E-mail Fraud Detection

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