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@article{205655,
        author = {MSHILPA and Mohammed Fasi Uddin and Mohammed Irfan Hussain Siddiqui and Mohammed Sofiyan},
        title = {Email Shield : Spam and Scam Detection Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {7836-7842},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205655},
        abstract = {Email Shield is a machine learning- based security system designed to automatically detect spam and scam emails with high accuracy. The system processes incoming emails through multiple stages beginning with text preprocessing, where noise removal, tokenization, and stop word elimination are performed. The cleaned text is then transformed into numerical form using TF-IDF and word embeddings within the feature extraction module. These features are passed into machine learning classifiers including Naive Bayes, Logistic Regression, and Support Vector Machine (SVM) to predict whether an email is spam, not spam, scam, or safe. To enhance detection reliability, a Threat Intelligence Module performs keyword-based scam identification and sender reputation analysis. The Decision Engine combines classification results and threat intelligence signals to generate a final classification along with a confidence score.},
        keywords = {Email Security, Machine Learning, Spam Detection, Scam Detection, Naive Bayes, Support Vector Machine, TF-IDF, Threat Intelligence, Natural Language Processing, Phishing Detection},
        month = {June},
        }