Large Language Models in Email Forensics: Futuristic Pattern Analysis and Approaches

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.

1. Introduction to Large Language Model (LLM): Fundamentals, Issues and Challenges 2. Introduction to Large Language Models and its Integration into Digital Forensic in Email Forensic3. Design of multimodal LLMs with transfer learning and computer vision architectural perspective4. Role of Explainable Large Language Models (LLMs) in Email Forensics –From Decision Making Perspective5. Security and privacy concerns of automated e-mail investigation based on LLMs and keyword searching techniques6. Combining NLP with LLMs to Assess Potential Threats and Email Protection7. Exploring Open-Source Technologies for Email Forensics: A Comprehensive Guide for User Activity and Email Traffic Analysis8. Explainable LLMs for Email Forensics: A Catalyst for Trust in Email Forensic Analysis9. Transformative potential and challenges of large language models in email forensics: applications, security, and future directions10. Role of keyword searching in Large Language Models (LLMs): Past, Present and Future11. Text to Text Transfer Transformer T5 Email Forensics: A Case Study on MGM Resort Cyber Attack12. Introduction to E-mail Forgery: Understanding the Basics and Significance13. LARGE LANGUAGE MODEL IN EMAIL FRAUD UNDER INSURANCE SECTOR14. Case Study: LLM Integration in an E-Commerce Company15. Design of Multimodal Large Language Models (LLMs) with Transfer Learning and Computer Vision: An Architectural Perspective16. Design of Multimodal Large Language Models (LLMs) with Transfer Learning and Computer Vision: An Architectural Perspective

Rajesh Kumar Dhanaraj is a professor at Symbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), India. He holds a PhD in information and communication engineering from Anna University, India. He has published more than 35 articles in various journals and conference proceedings and book chapters. His research interests include cyber-physical systems, wireless sensor networks, and cloud computing. He is an expert advisory panel member of Texas Instruments Inc., USA.

Malathy Sathyamoorthy is an associate professor in the Department of Information Technology, KPR Institute of Engineering and Technology, India. She holds a PhD in information and communication engineering from Anna University. She completed her BE and ME in computer science and engineering from Velalar College of Engineering and Technology, India. Her research interests include wireless networks, Internet of Things, and machine learning. She has published more than 20 research papers in international journals and conferences.

S. Poonkuntran holds a BE in information technology from Bharathidasan University, India, and MTech and PhD degrees in computer and information technology from Manonmaniam Sundaranar University, India. Currently he is professor at VIT Bhopal University, India, and dean of the School of Computing Science and Engineering. Dr Poonkuntran has more than a decade of experience in teaching and research and successfully executed three funded research grant projects from the Indian Space Research Organization, Defense Research Development Organization, and Ministry of New and Renewable Energy, Government of India. He has also received two seminar grants from Anna University and the All-India Council for Technical Education-Indian Society for Technical Education. He has published more than 90 technical articles, 8 books, 2 chapters, and 5 patents. He is a recipient of Cognizant Best Faculty Award 2017–18 and served as a State Level Student Coordinator for Region VII, CSI, India in 2016–17. He is a lifetime member of IACSIT, Singapore, CSI, India, and ISTE, India. His research interests include information security, computer vision, artificial intelligence, and machine learning.

S. Aanjan Kumar holds a doctorate in botnet security and an ME in software engineering, both from Anna University. He has academic experience spanning a decade and has worked at various levels up to associate professor, UG-HoD. He has authored 17 publications in peer-reviewed international and national journals with high impact factors and 11 publications in various international conferences held in India and abroad. Dr Aanjankumar has authored the books Graph Theory and Applications, which is included in Anna University’s syllabus, and Botnet Evolution, which has been adopted by Amity University for web security reference. He is also a reviewer for SCI journals and has 2 SCI journal publications with an impact factor above 3, 7 Scopus-indexed publications, 1 book chapter, and 1 Indian patent and 1 UK design patent that he has authored and published. His main areas of research include software engineering, cyber forensics, network security, and human–computer interaction.

Monoj Kumar Muchahari has over 10 years of experience in both teaching and industry. His research interests include cloud computing, trust management, AI, and machine learning. He has authored several journal articles, conference papers, and book chapters in these areas. He has also served as a reviewer for many reputable journals. Dr Muchahari He has organized various webinars and training programs on emerging technologies. He also has been invited for talks and to chair conference sessions.