Context-Aware Phishing Email Detection Using Machine Learning and NLP

Computer Science > Cryptography and Security [Submitted on 28 Mar 2026] Context-Aware Phishing Email Detection Using Machine Learning and NLP Amitabh Chakravorty, Matthew Price, Nelly Elsayed, Zag ElSayed 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. Comments: 6 pages, 5 figures, under review Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.27326 [cs.CR]   (or arXiv:2603.27326v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.27326 Focus to learn more Submission history From: Nelly Elsayed [view email] [v1] Sat, 28 Mar 2026 16:13:48 UTC (670 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)