Improve the robustness of algorithm under adversarial environment by moving target defense
Traditional machine learning models works in peace environment,assuming that training data and test data share the same distribution.However,the hypothesis does not hold in areas like malicious document detection.The enemy attacks the classification algorithm by modifying the test samples so that th...
Saved in:
| Main Authors: | Kang HE, Yuefei ZHU, Long LIU, Bin LU, Bin LIU |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
POSTS&TELECOM PRESS Co., LTD
2020-08-01
|
| Series: | 网络与信息安全学报 |
| Subjects: | |
| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2020052 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Moving target defense against adversarial attacks
by: Bin WANG, et al.
Published: (2021-02-01) -
AMTD:a way of adaptive moving target defense
by: Danjun LIU, et al.
Published: (2018-01-01) -
Research progress on moving target defense for SDN
by: Jinglei TAN, et al.
Published: (2018-07-01) -
Optimal strategy selection method for moving target defense based on signaling game
by: Lyu JIANG, et al.
Published: (2019-06-01) -
A Moving Target Defense Strategy against Load Redistribution Attacks
by: Quanpeng HE, et al.
Published: (2024-09-01)