Support vector machines resilient against training data integrity attacks

作者：

Highlights：

• Support Vector Machines are designed to withstand noise in data.

• But they are vulnerable to integrity attacks by adversaries.

• Projecting data to lower dimensional spaces in specific directions may reduce the adversary’s effects.

• Game theory can be used to predict the adversary’s actions and take proactive precautions.

摘要

•Support Vector Machines are designed to withstand noise in data.•But they are vulnerable to integrity attacks by adversaries.•Projecting data to lower dimensional spaces in specific directions may reduce the adversary’s effects.•Game theory can be used to predict the adversary’s actions and take proactive precautions.

论文关键词：Support Vector Machines,Integrity attack

论文评审过程：Received 2 January 2019, Revised 31 May 2019, Accepted 29 July 2019, Available online 1 August 2019, Version of Record 10 August 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.106985