Recent Research Reviews Journal
Abstract
Spam mails are some of the critical issues in cybersecurity. In this regard, they have adverse effects on the use of resources and productivity of users. This study suggests an artificial intelligence technique in classifying spam mail by utilizing machine learning and deep learning methods. Such include decision tree, naive bayes, support vector machine, and deep learning known as LSTM. Performance analysis of the model will be dependent on aspects such as accuracy, precision, recall, and F1 score among others. Based on the results in this study, the deep learning techniques such as LSTM provide high accuracy rates of about 98.5%. Similarly, the integration of the Convolutional Neural Network and LSTM offers high accuracy of about 99%. However, Naive Bayes shows low accuracy in the short time frame of training.
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