The DOI matches its Crossref record; 30 of the 32 references it cites were found in a registry or an index.
An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach
Open accessAbstract
Phishing and spam detection is a long standing challenge that has been the subject of much academic research. Large Language Models (LLM) have vast potential to transform society and provide new and innovative approaches to solve well-established challenges. Phishing and spam have caused financial hardships and lost time and resources to email users all over the world and frequently serve as an entry point for ransomware threat actors. While detection approaches exist, especially heuristic-based approaches, LLMs offer the potential to venture into a new unexplored area for understanding and solving this challenge. LLMs have rapidly altered the landscape from business, consumers, and throughout academia and demonstrate transformational potential for the potential of society. Based on this, applying these new and innovative approaches to email detection is a rational next step in academic research. In this work, we present IPSDM, an improved phishing spam detection model based on fine-tuning the BERT family of models to specifically detect phishing and spam email. We demonstrate our fine-tuned version, IPSDM, is able to better classify emails in both unbalanced and balanced datasets.
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“Jamal and Wimmer (2023) presents a model named Improved Phishing and Spam Detection Model (IPSDM), a fine-tuned model based on DistilBERT and RoBERTa. They emphasizes the potential of LLMs to revolutionize the field of email security and suggests that these models can be valuable tools for improving the security of information systems.”
From When LLMs meet cybersecurity: a systematic literature review (2025) · cites this paper as Jamal and Wimmer (2023)
Citations
Cites
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A benchmark study of machine learning models for online fake news detection
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Spam Email Detection Using Deep Learning Techniques
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Spam Detection Using Bidirectional Transformers and Machine Learning Classifier Algorithms
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Cited by
Sorted by citations
A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly
Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., & Zhang, Y. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100211 · Cited by 1,083
When LLMs meet cybersecurity: a systematic literature review
Zhang, J., Bu, H., Wen, H., Liu, Y., Fei, H., Xi, R., Li, L., Yang, Y., Zhu, H., & Meng, D. (2025) · Cybersecurity · doi:10.1186/s42400-025-00361-w · Cited by 383
Large Language Models for Cyber Security: A Systematic Literature Review
Xu, H., Wang, S., Li, N., Wang, K., Zhao, Y., Chen, K., Yu, T., Liu, Y., & Wang, H. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3769676 · Cited by 189
Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities
Ferrag, M. A., Alwahedi, F., Battah, A., Cherif, B., Mechri, A., Tihanyi, N., Bisztray, T., & Debbah, M. (2025) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2025.01.001 · Cited by 148
From Vulnerability to Defense: The Role of Large Language Models in Enhancing Cybersecurity
Kasri, W., Himeur, Y., Alkhazaleh, H. A., Tarapiah, S., Atalla, S., Mansoor, W., & Al-Ahmad, H. (2025) · Computation · doi:10.3390/computation13020030 · Cited by 87
Metadata
- Authors
- Jamal, S., Wimmer, H., & Sarker, I.
- Affiliations
- Georgia Southern University (US)
- Edith Cowan University (AU)
- Venue
- Research Square
- Publisher
- Research Square Platform LLC
- Published
- 2023-12-01
- OpenAlex
- W4389245454
- Cited by
- 27
- References
- 32
- Citations by year
- 2026: 3 · 2025: 16 · 2024: 7 · 2023: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- Research Square (acceptedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- phishing detection, email spam detection, large language models, BERT fine-tuning, email classification, class imbalance
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