An Improved Convolutional Neural Network-Based Spam Recognition Model
Jinyuan Liu · Advances in computer science research · 2024
Spam is one of the significant threats to cyber security by not only sending unwanted messages but also by potentially carrying viruses.Conventional spam detection methods, such as keyword matching and rulebased filtering, are less effective since spammers could advance their method to bypass these simple detection approaches.Machine learning algorithms can quickly and effectively identify subtle relationships among email texts, thus providing a more promising defense against spams.In this work, a Convolutional Neural Network (CNN)-based approach to spam recognition, leveraging the power of deep learning to process and analyze email content.This method is designed to address the shortcomings of traditional methods by employing a deep learning architecture that can generalize well to new, unseen data.Through detailed experimental analysis, the paper demonstrates that the proposed model not only achieves high performance in detecting spam but also significantly reduces the incidence of false positives, which is crucial for maintaining user trust and ensuring that normal emails will not be wrongly classified as spam.