Cyber threat prediction using dynamic heterogeneous graph learning-Reference-Cited by-同舟云学术

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Cyber threat prediction using dynamic heterogeneous graph learning

Published:2022-03 Issue: Volume:240 Page:108086
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Zhao Jun,Shao Minglai,Wang Hong,Yu Xiaomei,Li Bo,Liu Xudong

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference49 articles.

1. Evidential classification and feature selection for cyber-threat hunting;Beechey;Knowl.-Based Syst.,2021

2. Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues;Aldweesh;Knowl.-Based Syst.,2020

3. F. Liu, Y. Wen, D. Zhang, X. Jiang, X. Xing, D. Meng, Log2vec: A Heterogeneous graph embedding based approach for detecting cyber threats within enterprise, in: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019, pp. 1777–1794.

4. Multi-attributed heterogeneous graph convolutional network for bot detection;Zhao;Inform. Sci.,2020

5. Metric selection and anomaly detection for cloud operations using log and metric correlation analysis;Farshchi;J. Syst. Softw.,2018

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2. A survey of large language models for cyber threat detection;Computers & Security;2024-10

3. Cyber security attack recognition on cloud computing networks based on graph convolutional neural network and graphsage models;Results in Control and Optimization;2024-06

4. Hacker group identification based on dynamic heterogeneous graph node update;Applied Soft Computing;2024-06

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