NDLI: Apply randomization in KNN to make the adversary harder to attack the classifier

Content Provider	IEEE Xplore Digital Library
Author	Hui Yu Chan, P.P.K. Ng, W.W.Y. Yeung, D.S.
Copyright Year	2010
Description	Author affiliation: Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China (Hui Yu; Chan, P.P.K.; Ng, W.W.Y.; Yeung, D.S.)
Abstract	Adversarial pattern classification has been proposed in [1]. In adversarial pattern classification, an adversary wants to change the attributes of an instance to let the classifier make a wrong classification to gain utility. But to disguise an instance an adversary has to pay a cost. The adversary will never do this if the cost is higher than the utility. Adversarial classification systems include examples such as biometric personal authentication, intrusion detection in computer networks and spam filtering. Several methods have been proposed to tackle adversarial pattern classification problem using multiple $classifiers^{[6]}$ and $randomization^{[1]}$ methodology. In this paper, we apply the adversarial pattern classification model to KNN classifier. We assume the existence of an adversary in the KNN classifier and add randomization into the KNN classifier. Experiments to simulate the two-player game between classifier and adversary were perform. Experimental results show that adding randomization could make the adversary harder to attack the classifier.
Starting Page	179
Ending Page	183
File Size	125773
Page Count	5
File Format	PDF
ISBN	9781424465262
e-ISBN	9781424465279
DOI	10.1109/ICMLC.2010.5581070
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Classification algorithms Pattern classification Data mining Presses Machine learning Accuracy Cybernetics Randomization Adversarial pattern classification Attack KNN
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Tree classifier in spectral space

Associative Classifiers for Predictive Analytics: Comparative Performance Study

Detecting and adapting to drifting concepts

An L-infinity Norm Visual Classifier

Smile detection from still images using KNN algorithm

Enhanced leukemia cancer classifier algorithm

A novel text classification based on Mahalanobis distance

WAIRS: improving classification accuracy by weighting attributes in the AIRS classifier

Gene expression data classification using SVM-KNN classifier

Apply randomization in KNN to make the adversary harder to attack the classifier

Similar Documents

Tree classifier in spectral space

Associative Classifiers for Predictive Analytics: Comparative Performance Study

Detecting and adapting to drifting concepts

An L-infinity Norm Visual Classifier

Smile detection from still images using KNN algorithm

Enhanced leukemia cancer classifier algorithm

A novel text classification based on Mahalanobis distance

WAIRS: improving classification accuracy by weighting attributes in the AIRS classifier

Gene expression data classification using SVM-KNN classifier

Apply randomization in KNN to make the adversary harder to attack the classifier