Enhanced Spam Mail Detection Using Advanced Stylometric Features

Abstract

Phishing and spam emails have become one of the most persistent challenges in today’s digital age, often taking advantage of human trust and slipping past conventional security filters. Traditional approaches such as rule-based checks and keyword spotting tend to fall short, especially against sophisticated attacks that disguise themselves using subtle language tricks like word distortion or impersonation. To overcome these limitations, this study introduces a phishing detection model that relies on stylometric analysis, a technique that examines the unique way people write. By analyzing features such as sentence structure, word choice, punctuation use, and writing consistency, the system can detect unusual patterns that suggest malicious intent. Unlike surface-level filters, this method goes deeper into the author’s writing style, making it more resilient to evolving phishing tactics. The model also incorporates contextual awareness by comparing suspicious emails against the sender’s historical writing style, which helps reduce false alarms. Experimental results show that this approach achieves stronger detection rates than traditional techniques, highlighting the value of stylometry as a scalable, adaptive, and intelligent layer of protection. This research not only improves accuracy but also demonstrates a practical way to enhance trust and security in digital communication.