Automatically predicting cyber attack preference with attributed heterogeneous attention networks and transductive learning-Reference-Cited by-同舟云学术

Automatically predicting cyber attack preference with attributed heterogeneous attention networks and transductive learning

Published:2021-03 Issue: Volume:102 Page:102152
ISSN:0167-4048
Container-title:Computers & Security
language:en
Short-container-title:Computers & Security

Author:

Zhao Jun,Liu Xudong,Yan Qiben,Li Bo,Shao Minglai,Peng Hao,Sun Lichao

Funder

Beihang University

Publisher

Elsevier BV

Subject

Law,General Computer Science

Reference33 articles.

2. Using the pattern-of-life in networks to improve the effectiveness of intrusion detection systems;Aparicio,2017

3. Execution anomaly detection in large-scale systems through console log analysis;Bao;J. Syst. Softw.,2018

4. Predicting and detecting emerging cyberattack patterns using streamworks;Chin Jr.,2014

5. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K., 2018. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805.

Cited by 15 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. Graph neural network‐based attack prediction for communication‐based train control systems;CAAI Transactions on Intelligence Technology;2024-02-03

2. STIOCS: Active learning-based semi-supervised training framework for IOC extraction;Computers and Electrical Engineering;2023-12

3. Cyber Security Threats to Educational Institutes: A Growing Concern for the New Era of Cybersecurity;International Journal of Data Science and Big Data Analytics;2023-05-11

4. A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications;Journal of Big Data;2023-04-14

5. Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks;Computers, Materials & Continua;2023