Machine Learning with IoT Based Email Phishing Detection

Abstract

Email is a popular method for communicating for individuals as well as professionals. Email is routinely used to send sensitive personal data such as passwords for accounts, credit report specifics, and banking information. As a result, cybercriminals may profit from the information, making them lucrative. Con artists utilise phishing to act as trusted sources in order to gain confidential information from people. By employing false pretences, the sender or a phishing email might deceive you into exposing personal information. This work approaches the detection of compromised emails as a classification issue, and it demonstrates how machine learning algorithms are utilized to determine whether emails are phished or not. The SVM classifier has a maximum accuracy of 0.998 percent in classifying emails.