• Patil Divya
• Z Monica Prakashitha

Spam emails pose a significant cybersecurity threat, often serving as vectors for phishing attacks, malware distribution, and fraudulent schemes. Traditional rule-based and machine learning (ML) approaches struggle with evolving spam tactics and language variations. This project introduces an advanced spam detection system leveraging Natural Language Processing (NLP) and deep learning techniques, including Transformer models such as BERT and GPT. The system enhances email classification accuracy by incorporating sophisticated text understanding mechanisms. Additionally, it supports multi-language spam detection using diverse datasets, including SpamAssassin, Enron, and multilingual corpora. To enable real-time filtering, a browser extension is developed to analyze emails as they arrive and communicate with a Flask-based backend for classification. The system undergoes extensive preprocessing, including tokenization, stopword removal, and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization. It integrates multiple models, including Naïve Bayes, Logistic Regression, Recurrent Neural Networks (RNNs), and Transformers, to ensure high precision and recall. The deployment utilizes Python, TensorFlow, NLTK, and Scikit-learn, with Flask and Docker for scalability. Experimental results demonstrate that Transformer-based models significantly outperform traditional approaches, reducing false positives and improving spam detection in multiple languages. The real-time filtering capability enhances cybersecurity by proactively blocking spam before it reaches users. This project contributes to developing an intelligent, scalable, and multilingual email security solution that adapts to evolving cyber threats.

Spam Detection, AI-Powered Email Security, Natural Language Processing (NLP), Transformer Models, BERT, GPT, Machine Learning, Recurrent Neural Networks (RNNs), Multi-Language Spam Filtering, Real-Time Email Classification, Cybersecurity, Browser Extension, Flask API, Deep Learning.

"AI-Driven Spam Email Detection: A Multilingual and Real-Time Approach with Transformer Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.c217-c222, March-2025, Available :http://www.jetir.org/papers/JETIR2503226.pdf

"AI-Driven Spam Email Detection: A Multilingual and Real-Time Approach with Transformer Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppc217-c222, March-2025, Available at : http://www.jetir.org/papers/JETIR2503226.pdf