Text Email Spam Adversarial Attack Detection and Prevention Based on Deep Learning

This research paper addresses the challenges of email spam detection, particularly in the face of evolving adversarial attacks that exploit weaknesses in traditional methods.It proposes a hybrid approach combining Convolutional Neural Networks (CNN) for feature extraction and Boost for classification, aiming to improve detection performance against adversarial spam.The proposed CNN-Boost combination outperforms conventional spam detection methods significantly as shown in the primary findings.The Ling-Spam dataset used for training and evaluation, which includes labeled email data distinguishing between spam and legitimate emails.the model secures 99% accuracy and retains 96% accuracy against adversarial samples demonstrating its ability to safeguard classification performance when facing attacks.Recognizing adversarial examples at 85%, the model surpasses logistic regression and support vector machines which exhibit detection rates of 25% and 40%, respectively.In conclusion, the proposed CNN-Boost hybrid model significantly improves the accuracy and reliability of spam detection, even under adversarial conditions.The key innovation lies in integrating CNN for robust feature extraction with XGBoost for effective classification, particularly to mitigate adversarial email spam attacks.This hybrid model offers a superior balance between feature learning and classification accuracy, addressing limitations in traditional approaches.This approach not only boosts detection performance but also enhances the system's adaptability to emerging threats.