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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/6196

Title: Anomaly based intrusion detection for a biometric identification system using neural networks
Authors: Mgabile, T
Msiza, IS
Dube, E
Keywords: Biometric systems
Neural network
Machine learning
Intrusion detection
Issue Date: Oct-2012
Publisher: Planetary Scientific Research Center (PSRC)
Citation: Mgabile, T, Msiza, IS and Dube, E. Anomaly based intrusion detection for a biometric identification system using neural networks. Planetary Scientific Research Centre, Dubai (UAE), 6-7 October 2012
Series/Report no.: Workflow;9702
Abstract: This manuscript presents a supervised machine learning approach in the identification of network attacks on a fingerprint biometric system. To reduce the problem of malicious acts on a biometric system, this manuscript proposes an intrusion detection technique that analyses the fingerprint biometric network traffic for evidence of intrusion. The neural network algorithm that imitates the way a human brain works is used in this study to classify normal traffic and learn the correct traffic pattern on a fingerprint biometric system. The aim of the study is to observe the ability of the neural network in the detection of known and unknown attacks without using a vast amount of training data. The results of the neural network model had a classification rate of 98 %, which translates to a false positive rate of 2%.
Description: Planetary Scientific Research Centre, Dubai (UAE), 6-7 October 2012
URI: http://psrcentre.org/images/extraimages/1012138.pdf
http://hdl.handle.net/10204/6196
ISBN: 978-93-82242-09-3
Appears in Collections:Human factors
Information security
General science, engineering & technology

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