Mixed Strategy Game Model Against Data Poisoning Attacks

被引：0

作者：

Ou, Yifan ^{[1
]}

Samavi, Reza ^{[2
]}

机构：

[1] McMaster Univ, Hamilton, ON, Canada

[2] McMaster Univ, Vector Inst Artificial Intelligence Hamilton, Toronto, ON, Canada

来源：

2019 49TH ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS WORKSHOPS (DSN-W) | 2019年

基金：

加拿大自然科学与工程研究理事会;

关键词：

Adversarial Machine Learning; Poisoning Attacks; Game Theory; Nash Equilibrium; Secure Learning;

D O I：

10.1109/DSN-W.2019.00015

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper we use game theory to model poisoning attack scenarios. We prove the non-existence of pure strategy Nash Equilibrium in the attacker and defender game. We then propose a mixed extension of our game model and an algorithm to approximate the Nash Equilibrium strategy for the defender. We then demonstrate the effectiveness of the mixed defence strategy generated by the algorithm, in an experiment.

引用

页码：39 / 43

页数：5

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