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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Bibliografiske detaljer

Principais autores:	Zhi Qiang Wang, Haopeng Wang, Abdulmotaleb El Saddik
Format:	Artigo
Sprog:	Inglês
Udgivet:	IEEE 2024-01-01
Serier:	IEEE Access
Fag:	Cybersecurity insider threat deep learning transformer BERT RoBERTa
Online adgang:	https://ieeexplore.ieee.org/document/10721229/
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