The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Revolutionizing Cyber Threat Detection With Large Language Models: A Privacy-Preserving BERT-Based Lightweight Model for IoT/IIoT Devices
Open accessAbstract
The field of Natural Language Processing (NLP) is currently undergoing a revolutionary transformation driven by the power of pre-trained Large Language Models (LLMs) based on groundbreaking Transformer architectures. As the frequency and diversity of cybersecurity attacks continue to rise, the importance of incident detection has significantly increased. IoT devices are expanding rapidly, resulting in a growing need for efficient techniques to autonomously identify network-based attacks in IoT networks with both high precision and minimal computational requirements. This paper presents SecurityBERT, a novel architecture that leverages the Bidirectional Encoder Representations from Transformers (BERT) model for cyber threat detection in IoT networks. During the training of SecurityBERT, we incorporated a novel privacy-preserving encoding technique called Privacy-Preserving Fixed-Length Encoding (PPFLE). We effectively represented network traffic data in a structured format by combining PPFLE with the Byte-level Byte-Pair Encoder (BBPE) Tokenizer. Our research demonstrates that SecurityBERT outperforms traditional Machine Learning (ML) and Deep Learning (DL) methods, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), in cyber threat detection. Employing the Edge-IIoTset cybersecurity dataset, our experimental analysis shows that SecurityBERT achieved an impressive 98.2% overall accuracy in identifying fourteen distinct attack types, surpassing previous records set by hybrid solutions such as GAN-Transformer-based architectures and CNN-LSTM models. With an inference time of less than 0.15 seconds on an average CPU and a compact model size of just 16.7MB, SecurityBERT is ideally suited for real-life traffic analysis and a suitable choice for deployment on resource-constrained IoT devices.
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Cites
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
HuggingFace's Transformers: State-of-the-art Natural Language Processing
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Ferrag, M. A., Friha, O., Hamouda, D., Maglaras, L., & Janicke, H. (2022) · IEEE Access · doi:10.1109/access.2022.3165809 · Cited by 1,181
Self-citationDeep learning for cyber security intrusion detection: Approaches, datasets, and comparative study
Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2020) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2019.102419 · Cited by 1,130
Self-citationFELIDS: Federated learning-based intrusion detection system for agricultural Internet of Things
Friha, O., Ferrag, M. A., Shu, L., Maglaras, L., Choo, K.-K. R., & Nafaa, M. (2022) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2022.03.003 · Cited by 314
Self-citationMoustafa, N., Koroniotis, N., Keshk, M., Zomaya, A. Y., & Tari, Z. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3280465 · Cited by 266
Securing Critical Infrastructures: Deep-Learning-Based Threat Detection in IIoT
Yu, K., Tan, L., Mumtaz, S., Al-Rubaye, S., Al-Dulaimi, A., Bashir, A. K., & Khan, F. A. (2021) · IEEE Communications Magazine · doi:10.1109/mcom.101.2001126 · Cited by 251
DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection
Chen, Y., Ding, Z., Alowain, L., Chen, X., & Wagner, D. (2023) · Proceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses · doi:10.1145/3607199.3607242 · Cited by 238
Cited by
When LLMs meet cybersecurity: a systematic literature review
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“In addition, ChatGPT is also used to detect browser history, Windows event logs, and interactions with cloud platform machines.”
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
Self-citationYigit, Y., Ferrag, M. A., Ghanem, M. C., Sarker, I. H., Maglaras, L. A., Chrysoulas, C., Moradpoor, N., Tihanyi, N., & Janicke, H. (2025) · Sensors · doi:10.3390/s25061666 · Cited by 69
Self-citationUnveiling Shadows: Harnessing Artificial Intelligence for Insider Threat Detection
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Metadata
- Authors
- Ferrag, M. A., Ndhlovu, M., Tihanyi, N., Cordeiro, L. C., Debbah, M., Lestable, T., & Thandi, N. S.
- Venue
- IEEE Access
- Published
- 2024
- Volume
- 12
- Pages
- 23733-23750
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4391582407
- Cited by
- 312
- References
- 36
- FWCI
- 52.05
- Type
- journal article
- Language
- en
- Open access
- gold
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- Ferrag, M. A., Ndhlovu, M., Tihanyi, N., Cordeiro, L. C., Debbah, M., Lestable, T., & Thandi, N. S. (2024). Revolutionizing Cyber Threat Detection With Large Language Models: A Privacy-Preserving BERT-Based Lightweight Model for IoT/IIoT Devices. IEEE Access. https://doi.org/10.1109/access.2024.3363469
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