Chunrui Zhang
Chunrui Zhang has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
Dechen Zhan
2 shared publicationsMingyong Yin
2 shared publications- Shen Wang
Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
2 shared publications - Dechen Zhan
Harbin Institute of Technology, Harbin, China
1 shared publication - Fang Lou
Institution of Computer Application, China Academy of Engineering Physics, Mianyang 621900, China
1 shared publication - Gang Wang
Institute of Computer Application, China Academy of Engineering Physics, Mianyang 621000, China
1 shared publication
Latest Publications
- 9 March 2023
- Published by Elsevier in Computer Networks
- Vol. 227
Cross-domain network attack detection enabled by heterogeneous transfer learning
- 9 March 2023
- Published by Elsevier in Computer Networks
- Vol. 227
Cross-domain network attack detection enabled by heterogeneous transfer learning
- Open Access
- 26 November 2021
- Published by Wiley in Security and Communication Networks
- Vol. 2021, 1-11
Detecting Insider Threat from Behavioral Logs Based on Ensemble and Self-Supervised Learning
Recent studies have highlighted that insider threats are more destructive than external network threats. Despite many research studies on this, the spatial heterogeneity and sample imbalance of input features still limit the effectiveness of existing machine learning-based detection methods. To solve this problem, we proposed a supervised insider threat detection method based on ensemble learning and self-supervised learning. Moreover, we propose an entity representation method based on TF-IDF to improve the detection effect. Experimental results show that the proposed method can effectively detect malicious sessions in CERT4.2 and CERT6.2 datasets, where the AUCs are 99.2% and 95.3% in the best case.
- 26 November 2021
- Published by Wiley in Security and Communication Networks
- Vol. 2021, 1-11
Open AccessDetecting Insider Threat from Behavioral Logs Based on Ensemble and Self-Supervised Learning
Recent studies have highlighted that insider threats are more destructive than external network threats. Despite many research studies on this, the spatial heterogeneity and sample imbalance of input features still limit the effectiveness of existing machine learning-based detection methods. To solve this problem, we proposed a supervised insider threat detection method based on ensemble learning and self-supervised learning. Moreover, we propose an entity representation method based on TF-IDF to improve the detection effect. Experimental results show that the proposed method can effectively detect malicious sessions in CERT4.2 and CERT6.2 datasets, where the AUCs are 99.2% and 95.3% in the best case.
- Open Access
Inferring Users’ Social Roles with a Multi-Level Graph Neural Network Model
Users of social networks have a variety of social statuses and roles. For example, the users of Weibo include celebrities, government officials, and social organizations. At the same time, these users may be senior managers, middle managers, or workers in companies. Previous studies on this topic have mainly focused on using the categorical, textual and topological data of a social network to predict users’ social statuses and roles. However, this cannot fully reflect the overall characteristics of users’ social statuses and roles in a social network. In this paper, we consider what social network structures reflect users’ social statuses and roles since social networks are designed to connect people. Taking an Enron email dataset as an example, we analyzed a preprocessing mechanism used for social network datasets that can extract users’ dynamic behavior features. We further designed a novel social network representation learning algorithm in order to infer users’ social statuses and roles in social networks through the use of an attention and gate mechanism on users’ neighbors. The extensive experimental results gained from four publicly available datasets indicate that our solution achieves an average accuracy improvement of 2% compared with GraphSAGE-Mean, which is the best applicable inductive representation learning method.
Open AccessInferring Users’ Social Roles with a Multi-Level Graph Neural Network Model
Users of social networks have a variety of social statuses and roles. For example, the users of Weibo include celebrities, government officials, and social organizations. At the same time, these users may be senior managers, middle managers, or workers in companies. Previous studies on this topic have mainly focused on using the categorical, textual and topological data of a social network to predict users’ social statuses and roles. However, this cannot fully reflect the overall characteristics of users’ social statuses and roles in a social network. In this paper, we consider what social network structures reflect users’ social statuses and roles since social networks are designed to connect people. Taking an Enron email dataset as an example, we analyzed a preprocessing mechanism used for social network datasets that can extract users’ dynamic behavior features. We further designed a novel social network representation learning algorithm in order to infer users’ social statuses and roles in social networks through the use of an attention and gate mechanism on users’ neighbors. The extensive experimental results gained from four publicly available datasets indicate that our solution achieves an average accuracy improvement of 2% compared with GraphSAGE-Mean, which is the best applicable inductive representation learning method.