Detecting Malicious Accounts in Online Developer Communities Using Deep Learning-Reference-Cited by-同舟云学术

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Detecting Malicious Accounts in Online Developer Communities Using Deep Learning

Published:2023-10-01 Issue:10 Volume:35 Page:10633-10649
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Gong Qingyuan¹^ORCID,Liu Yushan¹^ORCID,Zhang Jiayun¹^ORCID,Chen Yang¹^ORCID,Li Qi²^ORCID,Xiao Yu³^ORCID,Wang Xin¹^ORCID,Hui Pan⁴^ORCID

Affiliation:

1. Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China

2. Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing, China

3. Department of Communications and Networking, Aalto University, Espoo, Finland

4. Hong Kong University of Science and Technology, Hong Kong, China

Funder

National Natural Science Foundation of China

5GEAR project

Academy of Finland

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/10251471/10018883.pdf?arnumber=10018883

Reference72 articles.

1. Heterogeneous Graph Neural Networks for Malicious Account Detection

2. Detecting Fake Accounts in Online Social Networks at the Time of Registrations

3. Towards detecting anomalous user behavior in online social networks;viswanath;Proc USENIX Conf Secur Symp,2014

4. Adversarial label flips attack on support vector machines;xiao;Proc 20th Eur Conf Artif Intell,2012

5. FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance

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