AI Based Phishing Discrement for Immense E-Maildata

Cybersecurity assaults have become more commonplace in recent years. In the majority of these events, attackers successfully breached government agencies, renowned corporations, and the websites of social groups and politicians across numerous nations using numerous kinds of unsolicited emails as a kicker. Big email data analysis for the purpose of spam detection has received widespread attention. However, spam mail's camouflage technology is becoming more and more sophisticated, and the current detection techniques are unable to keep up with the more sophisticated deception techniques and the increasing volume of email. In this, we suggested to build a brand-new effective method called Spam Spoiler for classifying large amounts of four categories for email data: typical, dishonest, harassing, and disbelieving emails. The phase of expanding the sample and the phase of testing with sufficient samples are two crucial steps of the new methodology. According to experimental findings, Spam Spoiler outperformed other machine learning methods and used an LSTM with recurrent gradient units to obtain a classification accuracy of 98%. Since various themes are covered in email content analysis. While preserving the robustness and dependability of the categorization process, Spam Spoiler efficiently outperforms current approaches.