FedITD: A Federated Parameter-Efficient Tuning With Pre-Trained Large Language Models and Transfer Learning Framework for Insider Threat Detection

Insider threats cause greater losses than external attacks, prompting organizations to invest in detection systems. However, there exist challenges: 1) Security and privacy concerns prevent data sharing, making it difficult to train robust models and identify new attacks. 2) The diversity and unique...

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Detalles Bibliográficos

Principais autores:	Zhi Qiang Wang, Haopeng Wang, Abdulmotaleb El Saddik
Formato:	Artigo
Idioma:	Inglês
Publicado:	IEEE 2024-01-01
Series:	IEEE Access
Assuntos:	Cybersecurity insider threat deep learning transformer BERT RoBERTa
Acceso en liña:	https://ieeexplore.ieee.org/document/10721229/
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