典型文献
Poisoning attacks and countermeasures in intelligent networks:Status quo and prospects
文献摘要：
Over the past years,the emergence of intelligent networks empowered by machine learning techniques has brought great facilitates to different aspects of human life.However,using machine learning in intelligent net-works also presents potential security and privacy threats.A common practice is the so-called poisoning attacks where malicious users inject fake training data with the aim of corrupting the learned model.In this survey,we comprehensively review existing poisoning attacks as well as the countermeasures in intelligent networks for the first time.We emphasize and compare the principles of the formal poisoning attacks employed in different cat-egories of learning algorithms,and analyze the strengths and limitations of corresponding defense methods in a compact form.We also highlight some remaining challenges and future directions in the attack-defense confrontation to promote further research in this emerging yet promising area.
文献关键词：
中图分类号：
作者姓名：
Chen Wang;Jian Chen;Yang Yang;Xiaoqiang Ma;Jiangchuan Liu
作者机构：
School of Computer Science and Information Engineering,Hubei University,Wuhan,China;School of Electronic Information and Communications,Huazhong University of Science and Technology,Wuhan,430074,China;School of Computing Science at Simon Fraser University,British Columbia,Canada
文献出处：
引用格式：
[1]Chen Wang;Jian Chen;Yang Yang;Xiaoqiang Ma;Jiangchuan Liu-.Poisoning attacks and countermeasures in intelligent networks:Status quo and prospects)[J].数字通信与网络（英文）,2022(02):225-234
A类：
corrupting
B类：
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AB值：
0.653452
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