Email spam detection by deep learning models using novel feature selection technique and BERT

作者：Ghazala Nasreen, Muhammad Murad Khan, Muhammad Younus, Bushra Zafar, Muhammad Kashif Hanif · 发表于：Egyptian Informatics Journal · 年份：2024 · DOI：10.1016/j.eij.2024.100473 · 被引用次数：41 · 研究领域：Spam and Phishing Detection、Network Security and Intrusion Detection、Text and Document Classification Technologies

Due to the influx of advancements in technology and the increased simplicity of communication through emails, there has been a severe threat to the global economy and security due to upsurge in volume of unsolicited During the training of models, high-dimensional and redundant datasets may reduce the classification results of the model due to high memory costs and high computation. An important data processing technique is feature selection which helps in selecting relevant features and subsets of information from the dataset. Therefore, choosing efficient feature selection techniques is very important for the best performance of classification of a model. Moreover, most of the research has been performed using traditional machine learning techniques, which are not enough to deal with the huge amount of data and its variations. Also, spammers are becoming smarter with technological advancement. Therefore, there is a need for hybrid techniques consisting of deep learning and conventional algorithms to cope with these problems. We have proposed a novel scheme in this paper for email spam detection, which will result in an improved feature selection approach from the original dataset and increase the accuracy of the classifier as well. The literature has been studied to explore the efficient machine learning models that have been applied by different researchers for email spam detection and feature selection to acquire the best results. Our method, GWO-BERT, has given remarkable...