Large Language Models (LLMs) are advanced machine learning models designed to understand natural language processing (NLP) tasks using deep learning techniques on massive textual datasets. Recently, LLMs have attracted attention in digital forensics due to their potential to enhance investigation efficiency, improve evidence traceability, and overcome barriers faced by law enforcement agencies. Email communication plays a crucial role in both personal and official interactions, with millions of emails exchanged daily. This large volume makes manual inspection tedious and prone to oversight, creating opportunities for cyberattacks such as phishing, spamming, and ransomware. Traditional email forensic methods rely on keyword-based searches and predefined rule sets, which are often insufficient in identifying sophisticated threats.
This book highlights the advantages of integrating LLMs into email forensics as an automated solution. LLMs can analyze email patterns and user contexts, such as keystroke dynamics, sentiment, and native language nuances used in drafting messages. These capabilities allow for the identification of legitimate users and detection of altered content with greater accuracy. By leveraging LLMs, unexplored areas in manual forensics can be addressed, leading to more robust, efficient, and intelligent email forensic investigations that better support cybercrime detection and prevention.