Improving Email Security Through Machine Learning-Based Spam and Phishing Detection

Abstract

Electronic communication is growing at an exponential pace and it has become critical in this age to utilize email for personal and professional correspondence. But this rise has also generated a lot of spam and phishing attacks which have become real treats, not only for bottom users but for data privacy and cybersecurity. To deal with this issue, we developed SpamHybX to substantially improve the detection and classification of spam and phishing emails. SpamHybX exploits the combined prediction ability of Logistic Regression and Support Vector Machine (SVM) via a stacking ensemble method, utilizing the advantages of two classifiers for increasing the generalization capability and robustness. The model uses the TF- IDF text feature extraction method to transform the textual content of emails into numeric for effective labialization of legitimate and unsolicited messages. It is observed that SpamHybX outperforms individual model as evident from the Test Accuracy 98.64%, Precision 96.73%, Recall 98.67%, and F1-Score 97.69%. These metrics demonstrate that the model performs well in distinguishing spam and logging less FPs, and succeeding a promising trade-off between sensitivity and precision. The proposed hybrid model presents a robust, scalable and interpretable solution for email security applications that is believed to establish a roadmap for further studies of intelligent spam or phishing detection systems based on advanced ensemble learning algorithms. In summary, SpamHybX achieves a drastic enhancement towards email security by taking advantage of modern ML methods. The above-proposed methodology can be further extended to real-time spam filtering systems, which will enhance a safer and trustful environment for digital communication. In summary, SpamHybX provides a substantial enhancement of email security by successfully utilizing high-quality machine learning methods. The model could be extended to be used in real-time spam filtering systems, thereby inputting safer and trustful digital communication spaces.