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Reseach Article

A Review on Security Evaluation for Pattern Classifier against Attack

Published on December 2015 by Kunjali Pawar, Madhuri Patil
National Conference on Advances in Computing
Foundation of Computer Science USA
NCAC2015 - Number 4
December 2015
Authors: Kunjali Pawar, Madhuri Patil
5f7ca5c7-2caf-4f6c-8d13-ec1e9b8e69aa

Kunjali Pawar, Madhuri Patil . A Review on Security Evaluation for Pattern Classifier against Attack. National Conference on Advances in Computing. NCAC2015, 4 (December 2015), 15-18.

@article{
author = { Kunjali Pawar, Madhuri Patil },
title = { A Review on Security Evaluation for Pattern Classifier against Attack },
journal = { National Conference on Advances in Computing },
issue_date = { December 2015 },
volume = { NCAC2015 },
number = { 4 },
month = { December },
year = { 2015 },
issn = 0975-8887,
pages = { 15-18 },
numpages = 4,
url = { /proceedings/ncac2015/number4/23379-5045/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advances in Computing
%A Kunjali Pawar
%A Madhuri Patil
%T A Review on Security Evaluation for Pattern Classifier against Attack
%J National Conference on Advances in Computing
%@ 0975-8887
%V NCAC2015
%N 4
%P 15-18
%D 2015
%I International Journal of Computer Applications
Abstract

The systems which can be used for pattern classification are used in adversarial application, for example spam filtering, network intrusion detection system, biometric authentication. This adversarial scenario's exploitation may sometimes affect their performance and limit their practical utility. In case of pattern classification conception and contrive methods to adversarial environment is a novel and relevant research direction, which has not yet pursued in a systematic way. To address one main open issue: evaluating at contrive phase the security of pattern classifiers (for example the performance degradation under potential attacks which incurs during the operation). To propose a framework for evaluation of classifier security and also this framework can be applied to different classifiers on one of the application from the spam filtering,biometric authentication andnetwork intrusion detection.

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Index Terms

Computer Science
Information Sciences

Keywords

Machine Learning System Security Evaluation Adversarial Classification Arms-race Spam Filtering.