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

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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Bibliographic Details
Main Authors: Zhi Qiang Wang, Haopeng Wang, Abdulmotaleb El Saddik
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Cybersecurity
insider threat
deep learning
transformer
BERT
RoBERTa
Online Access:https://ieeexplore.ieee.org/document/10721229/
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https://ieeexplore.ieee.org/document/10721229/

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