Cover Image

Keystroke pressure based typing biometrics authentication system by combining ANN and ANFIS-based classifiers

Link to publisher's homepage at http://www.ieee.org/

Saved in:
Bibliographic Details
Main Authors: Hasimah, Ali, Martono, Wahyudi, Momoh Jimoh E., Salami
Other Authors: hasimahali@unimap.edu.my
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2012
Subjects:
Adaptive neuro-fuzzy inference system
Artificial Neural Network
Design and Development
False accept rate
False reject rate
Maximum pressure
Unauthorized users
Authentication
Legitimate users
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/18747
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.unimap-18747
record_format dspace
spelling my.unimap-187472012-04-10T07:47:47Z Keystroke pressure based typing biometrics authentication system by combining ANN and ANFIS-based classifiers Hasimah, Ali Martono, Wahyudi Momoh Jimoh E., Salami hasimahali@unimap.edu.my Adaptive neuro-fuzzy inference system Artificial Neural Network Design and Development False accept rate False reject rate Maximum pressure Unauthorized users Authentication Legitimate users Link to publisher's homepage at http://www.ieee.org/ Security of an information system depends to a large extent on its ability to authenticate legitimate users as well as to withstand attacks of various kinds. Confidence in its ability to provide adequate authentication is, however, waning. This is largely due to the wrongful use of passwords by many users. In this paper, the design and development of keystroke pressure-based typing biometrics for individual user's verification which based on the analysis of habitual typing of individuals is discussed. The paper examines the use of maximum pressure exerted on the keyboard and time latency between keystrokes as features to create typing patterns for individual users. Combining both an Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are adopted as classifiers to verify the authorized and unauthorized users based on extracted features of typing biometric. The effectiveness of the proposed system is evaluated based upon False Reject Rate (FRR) and False Accept Rate (FAR). A series of experiment shows that the proposed system that used combined classifiers produces promising result for both FAR and FRR. 2012-04-10T07:47:47Z 2012-04-10T07:47:47Z 2009-03-06 Working Paper p. 198-203 978-1-4244-4150-1 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5069216 http://hdl.handle.net/123456789/18747 en Proceedings of the 5th International Colloquium on Signal Processing and Its Applications (CSPA) 2009 Institute of Electrical and Electronics Engineers (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Adaptive neuro-fuzzy inference system
Artificial Neural Network
Design and Development
False accept rate
False reject rate
Maximum pressure
Unauthorized users
Authentication
Legitimate users
spellingShingle Adaptive neuro-fuzzy inference system
Artificial Neural Network
Design and Development
False accept rate
False reject rate
Maximum pressure
Unauthorized users
Authentication
Legitimate users
Hasimah, Ali
Martono, Wahyudi
Momoh Jimoh E., Salami
Keystroke pressure based typing biometrics authentication system by combining ANN and ANFIS-based classifiers
description Link to publisher's homepage at http://www.ieee.org/
author2 hasimahali@unimap.edu.my
author_facet hasimahali@unimap.edu.my
Hasimah, Ali
Martono, Wahyudi
Momoh Jimoh E., Salami
format Working Paper
author Hasimah, Ali
Martono, Wahyudi
Momoh Jimoh E., Salami
author_sort Hasimah, Ali
title Keystroke pressure based typing biometrics authentication system by combining ANN and ANFIS-based classifiers
title_short Keystroke pressure based typing biometrics authentication system by combining ANN and ANFIS-based classifiers
title_full Keystroke pressure based typing biometrics authentication system by combining ANN and ANFIS-based classifiers
title_fullStr Keystroke pressure based typing biometrics authentication system by combining ANN and ANFIS-based classifiers
title_full_unstemmed Keystroke pressure based typing biometrics authentication system by combining ANN and ANFIS-based classifiers
title_sort keystroke pressure based typing biometrics authentication system by combining ann and anfis-based classifiers
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/18747
_version_ 1643792517661982720
score 13.211869

Similar Items