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 IPSDM, a fine-tuned model based on DistilBERT and RoBERTa, and emphasizes the potential of LLMs to revolutionize email security.
“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)
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Precious, M. K. (2025) · i-manager s Journal on Computer Science · doi:10.26634/jcom.13.2.22310
Multi-Agent Phishing Detection And Deletion via Small VLM and LLM Reasoning
Ayrin, F. J., Rahman, M. M., Rahman, M. A., Akuthota, V., Ghosh, A. K., Khondaker, M. M. H., Haque, M. U., Quader, M. A., & Reza, M. T. (2026) · International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE) · doi:10.1109/icecte69292.2026.11429303
Mahesh, K. (2026) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/26mar858
Optimizing Email Filtering Systems: Addressing Challenges from AI-Generated Spam and Phishing
Duc, M. P., Tuan, K. N., & Thai, N. D. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-032-13254-3_4
Efficient Phishing Email Classification Using TinyBERT Model with OSINT-Based Features
Chowdary, P. R., Srinith, R. R., & Devendar Rao, B. (2026) · International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA) · doi:10.1109/aimla67915.2026.11522436
MiniLM-CNN-LSTM: A Lightweight Hybrid Transformer Model for Malicious URL Detection
Qazi, E. U. H., Faheem, M. H., & Almorjan, A. (2026) · Technologies · doi:10.3390/technologies14060316
Towards efficient deep learning assisted ensemble approach for spam comments detection in blogs data
Batool, A., Hyder, H., & Byun, Y.-C. (2026) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-026-05094-3
Abdelrahim, O., Fathalla, R., & El-Sonbaty, Y. (2026) · International Conference on Artificial Intelligence, Systems, and Emerging Technologies (ICAISET) · doi:10.1109/icaiset66439.2026.11541672
Spam Email Detection Using a Custom Multi-Head DistilBERT
Eghsais, W. T., Emary, E., & Abbas, H. (2026) · International Conference on Artificial Intelligence, Systems, and Emerging Technologies (ICAISET) · doi:10.1109/icaiset66439.2026.11541302
Sovereign AI Defense: Bilingual LLM-Driven Phishing Detection for Low-Resource Languages
Alauthman, M., Aldweesh, A. Y., Al–Qerem, A., Khalaf, B., & Alkasassbeh, M. S. (2026) · International Conference on Computational Intelligence Approaches and Applications (ICCIAA) · doi:10.1109/icciaa68481.2026.11543575
SpamLLM: Leveraging Large Language Models for Robust Spam Email Classification
He, Z., Liu, C., Hu, B., & Jia, D. (2026) · International Journal of Intelligent Systems · doi:10.1155/int/2309553
An LLM-Based Multilingual Phishing Detection Framework for Regional Languages
Sanjay, S., Krishna, N., Paramesh, G., & Kumar, K. K. (2026) · International Conference on AI-Driven Innovations & Applications (IC-AIDA) · doi:10.1109/ic-aida68291.2026.11564455
Saidi, F., Hazem, Z. B., & Güler, N. (2026) · Cluster Computing · doi:10.1007/s10586-026-06221-7
A Comparative Analysis of Selected Machine Learning Technique for Spam Email Detection
F, P. E., Muyideen, & Ayeni, J. K. (2026) · International journal of research and scientific innovation · doi:10.51244/ijrsi.2026.1306000247
Evaluating Multi-Agent Models for Social Engineering Attacks Prevention
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Detection and Prevention of Spam, Phishing, and Misbehavior in Social Systems Using Machine Learning
Houhamdi, Z., Athamena, B., Nazar, M. A., Siddique, A., & Ali, M. (2026) · International Conference on Emerging and Intelligent Technologies and Systems (EITS) · doi:10.1109/eits70066.2026.11609633
PhishCascade: Confidence-Gated Multi-agent Phishing Detection
Li, H. H., & Lin, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3015-0_17
Murtaza, A., Shahid, A., Hassan, S.-U., Rizvi, S. M. U., & Siraj, S. (2025) · International Journal of Innovations in Science and Technology · doi:10.33411/ijist/1279
Anti-phishing in the Era of Deepfakes
Kee, L. S., Marks, M., & Kayem, A. V. D. M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-34374-1_1
Automatic phishing detection methods in corporate email systems: a systematic review
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AI-Enhanced Email Security: A Novel Pipeline for Phishing Campaign Detection and Profiling
Saka, T., Vaniea, K. E., & Kökciyan, N. (2026) · ACM Transactions on Internet Technology · doi:10.1145/3848625
Text Message Filtering System Using Transformer-Based Deep Learning and Federated Learning
Lahoti, V., Pai, V., Hande, S., Nashipudimath, M., & Chavan, S. (2026) · International Conference on Innovation & Growth in Next-Gen Intelligent Technology & Engineering (IGNITE) · doi:10.1109/ignite69592.2026.11706547
Hybrid Transformer Architecture for Context-Aware Spam Email Classification
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Tepeli, M. (2026) · DÜMF Mühendislik Dergisi · doi:10.24012/dumf.1949394
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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