secure machine learning, a brief overview

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	secure machine learning, a brief overview
	Liao Xiaofeng; Ding Liping; Wang Yongji
	2011
会议名称	2011 5th International Conference on Secure Software Integration and Reliability Improvement - Companion, SSIRI-C 2011
会议录名称	2011 5th International Conference on Secure Software Integration and Reliability Improvement - Companion, SSIRI-C 2011
页码	26-29
会议日期	27-Jun-02
会议地点	Jeju Island, Korea, Republic of
收录类别	EI
出版地	United States
ISBN	9780769544540
部门归属	(1) National Engineering Research Center for Fundamental Software, Institute of Software, China; (2) State Key Laboratory of Computer Science, Institute of Software, China; (3) Graduate University, Chinese Academy of Sciences, Beijing 100049, China; (4) Information Engineering School, Nanchang University, Nanchang, Jiangxi, 330031, China
摘要	The purpose of this article is to give a brief overview on the current work towards the emerging research problem of secure machine learning. Machine learning technique has been applied widely in various applications especially in spam detection and network intrusion detection. Most existing learning schemes assume that the environment they settle in is benign. However this is not always true in the real adversarial decision-making situations where the future data sets and the training data set are no longer from the same population, due to the transformations employed by the adversaries. As more and more machine learning systems are put into use, it is imperative to consider the security of the machine learning system. As a emerging problem, it is attracting more and more researchers' attention. In this article, we present a brief overview on secure machine learning and current progress on developing secure machine learning algorithms. © 2011 IEEE.
关键词	c (Programming Language) Intrusion Detection Learning Systems Population Statistics Software Reliability
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/14231
专题	互联网软件技术实验室
推荐引用方式 GB/T 7714	Liao Xiaofeng,Ding Liping,Wang Yongji. secure machine learning, a brief overview[C]. United States,2011:26-29.

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