Quantifying Privacy Vulnerability to Socialbot Attacks: An Adaptive Non-Submodular Model-Reference-Cited by-同舟云学术

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Quantifying Privacy Vulnerability to Socialbot Attacks: An Adaptive Non-Submodular Model

Published:2020-07-01 Issue:3 Volume:8 Page:855-868
ISSN:2168-6750
Container-title:IEEE Transactions on Emerging Topics in Computing
language:
Short-container-title:IEEE Trans. Emerg. Topics Comput.

Author:

Li Xiang^ORCID,Smith J. David,Pan Tianyi^ORCID,Dinh Thang N.^ORCID,Thai My T.^ORCID

Funder

National Science Foundation

Defense Threat Reduction Agency

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Human-Computer Interaction,Information Systems,Computer Science (miscellaneous)

Link

https://ieeexplore.ieee.org/ielam/6245516/9185109/8364596-aam.pdf

Cited by 2 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. Text Mining-based Social-Psychological Vulnerability Analysis of Potential Victims To Cybergrooming: Insights and Lessons Learned;Companion Proceedings of the ACM Web Conference 2023;2023-04-30

2. Reformative Noise-Immune Neural Network for Equality-Constrained Optimization Applied to Image Target Detection;IEEE Transactions on Emerging Topics in Computing;2021

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