By Topic

Secure Machine Learning, a Brief Overview

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Xiaofeng Liao ; Nat. Eng. Res. Center for Fundamental Software, Inst. of Software, Beijing, China ; Liping Ding ; Yongji Wang

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.

Published in:

Secure Software Integration & Reliability Improvement Companion (SSIRI-C), 2011 5th International Conference on

Date of Conference:

27-29 June 2011