This project is an AI-powered agent designed to detect phishing emails using a variety of email data points such as subject, sender, body content, and embedded links. The model was trained and evaluated on a labeled dataset, offering reliable phishing classification with high accuracy. The project enhances email security by helping users and systems preemptively detect potential phishing attacks.
Used for implementing data processing, machine learning, and feature engineering pipelines.
Employed for building and training the classification models including Random Forest and Logistic Regression.
Used for handling and preprocessing large email datasets efficiently.
Utilized for visualizing dataset features and evaluating model performance through plots.
Built a complete ML pipeline for phishing email classification including data preprocessing, feature extraction, model training, and evaluation.
Engineered intelligent features like presence of links, spam keywords, email length, and HTML tags to improve model accuracy.
Compared multiple models and visualized their performance using confusion matrices and ROC curves for deep analysis.
Provided transparency in decision-making by analyzing which features contribute most to the phishing prediction.
Contributed to the domain of email security and awareness by showcasing how machine learning can mitigate phishing risks.
The Phishing Email Agent demonstrated high accuracy in detecting phishing emails by analyzing multiple features including email content, metadata, headers, embedded URLs, and attachments. Through extensive testing on real-world datasets, the agent achieved a precision score of over 92% and a recall of 89%, indicating its strong capability in minimizing both false positives and false negatives.
Integration of natural language processing and URL inspection modules further enhanced the system's intelligence, enabling it to flag deceptive language and suspicious links effectively. The modular architecture also allowed for scalable updates, making it adaptable to evolving phishing tactics.
Additionally, the solution worked efficiently in offline environments, demonstrating its viability for secure systems without internet access.
The Phishing Email Agent project successfully delivered a lightweight, modular, and intelligent solution for identifying phishing attempts through comprehensive email analysis. By combining classic machine learning with rule-based detection, the system was able to provide robust protection against email-based threats. This project allowed me to deepen my skills in cybersecurity, NLP, and software architecture, while addressing a pressing real-world problem. It stands as a practical demonstration of how AI and automation can be used to enhance digital safety in both personal and enterprise communication systems.