Context-Aware Phishing Email Detection Using Machine Learning and NLP
It offers a context-aware alternative to URL-based phishing detection, but the approach is incremental as it applies standard ML and NLP techniques to a known problem.
The paper presents a phishing email detection system using NLP and machine learning, achieving 95.41% accuracy and 94.33% F1-score with Logistic Regression on a combined dataset of 53,973 emails.
Phishing attacks remain among the most prevalent cybersecurity threats, causing significant financial losses for individuals and organizations worldwide. This paper presents a machine learning-based phishing email detection system that analyzes email body content using natural language processing (NLP) techniques. Unlike existing approaches that primarily focus on URL analysis, our system classifies emails by extracting contextual features from the entire email content. We evaluated two classification models, Naive Bayes and Logistic Regression, trained on a combined corpus of 53,973 labeled emails from three distinct datasets. Our preprocessing pipeline incorporates lowercasing, tokenization, stop-word removal, and lemmatization, followed by Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction with unigrams and bigrams. Experimental results demonstrate that Logistic Regression achieves 95.41% accuracy with an F1-score of 94.33%, outperforming Naive Bayes by 1.55 percentage points. The system was deployed as a web application with a FastAPI backend, providing real-time phishing classification with average response times of 127ms.