Per-sender neural network classifiers for email authorship validation

Abstract

Business email compromise and lateral spear phishing attacks are among modern organizations' most costly and damaging threats. While inbound phishing defenses have improved significantly, most organizations still trust internal emails by default, leaving themselves vulnerable to attacks from compromised employee accounts. In this work, we define and explore the problem of authorship validation: verifying whether a claimed sender actually authored a given email. Authorship validation is a lightweight, real-time defense that complements traditional detection methods by modeling per-sender writing style. Further, the paper presents a collection of new datasets based on the Enron corpus. These simulate inauthentic messages using both human-written and large language model-generated emails. The paper also evaluates two classifiers -- a Naive Bayes model and a character-level convolutional neural network (Char-CNN) -- for the authorship validation task. Our experiments show that the Char-CNN model achieves high accuracy and F1 scores under various circumstances. Finally, we discuss deployment considerations and show that per-sender authorship classifiers are practical for integrating into existing commercial email security systems with low overhead.