Few-shot Insider Threat Detection-Reference-Cited by-同舟云学术

Few-shot Insider Threat Detection

Published:2020-10-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM International Conference on Information & Knowledge Management
language:
Short-container-title:

Author:

Yuan Shuhan¹,Zheng Panpan²,Wu Xintao²,Tong Hanghang³

Affiliation:

1. Utah State University, Logan, UT, USA

2. University of Arkansas, Fayetteville, AR, USA

3. University of Illinois at Urbana-Champaign, Urbana, IL, USA

Funder

U.S. Department of Energy

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3340531.3412161

Reference6 articles.

1. VEWS

2. Jake Snell Kevin Swersky and Richard S. Zemel. 2017. Prototypical Networks for Few-shot Learning. In NIPS. Jake Snell Kevin Swersky and Richard S. Zemel. 2017. Prototypical Networks for Few-shot Learning. In NIPS.

3. Flood Sung Yongxin Yang Li Zhang Tao Xiang Philip H. S. Torr and Timothy M. Hospedales. 2017. Learning to Compare: Relation Network for Few-Shot Learning. In CVPR. Flood Sung Yongxin Yang Li Zhang Tao Xiang Philip H. S. Torr and Timothy M. Hospedales. 2017. Learning to Compare: Relation Network for Few-Shot Learning. In CVPR.

Cited by 19 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Contrastive Learning for Fraud Detection from Noisy Labels;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

2. Insider Threat Detection Model Enhancement Using Hybrid Algorithms between Unsupervised and Supervised Learning;Electronics;2024-03-03

3. BRITD: behavior rhythm insider threat detection with time awareness and user adaptation;Cybersecurity;2024-01-02

4. A Review of Recent Advances, Challenges, and Opportunities in Malicious Insider Threat Detection Using Machine Learning Methods;IEEE Access;2024

5. Insider Threat Defense Strategies: Survey and Knowledge Integration;Lecture Notes in Computer Science;2024