Insider threat detection using supervised machine learning algorithms
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering
Link
https://link.springer.com/content/pdf/10.1007/s11235-023-01085-3.pdf
Reference61 articles.
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2. Feng, X., Zhu, X., Han, Q.-L., Zhou, W., Wen, S., & Xiang, Y. (2022). Detecting vulnerability on iot device firmware: A survey. IEEE/CAA Journal of Automatica Sinica, 10(1), 25–41.
3. Zhang, J., Pan, L., Han, Q.-L., Chen, C., Wen, S., & Xiang, Y. (2021). Deep learning based attack detection for cyber-physical system cybersecurity: A survey. IEEE/CAA Journal of Automatica Sinica, 9(3), 377–391.
4. Sun, N., Zhang, J., Rimba, P., Gao, S., Zhang, L. Y., & Xiang, Y. (2018). Data-driven cybersecurity incident prediction: A survey. IEEE Communications Surveys and Tutorials, 21(2), 1744–1772.
5. Lin, G., Wen, S., Han, Q.-L., Zhang, J., & Xiang, Y. (2020). Software vulnerability detection using deep neural networks: a survey. Proceedings of the IEEE, 108(10), 1825–1848.
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1. A heterogeneous graph-based semi-supervised learning framework for access control decision-making;World Wide Web;2024-05-24
2. A Review of Recent Advances, Challenges, and Opportunities in Malicious Insider Threat Detection Using Machine Learning Methods;IEEE Access;2024
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