dc.contributor.author |
Mgabile, T
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|
dc.contributor.author |
Msiza, IS
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dc.contributor.author |
Dube, E
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dc.date.accessioned |
2012-10-19T12:56:03Z |
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dc.date.available |
2012-10-19T12:56:03Z |
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dc.date.issued |
2012-10 |
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dc.identifier.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 |
en_US |
dc.identifier.isbn |
978-93-82242-09-3 |
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dc.identifier.uri |
http://psrcentre.org/images/extraimages/1012138.pdf
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dc.identifier.uri |
http://hdl.handle.net/10204/6196
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dc.description |
Planetary Scientific Research Centre, Dubai (UAE), 6-7 October 2012 |
en_US |
dc.description.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%. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Planetary Scientific Research Center (PSRC) |
en_US |
dc.relation.ispartofseries |
Workflow;9702 |
|
dc.subject |
Biometric systems |
en_US |
dc.subject |
Neural network |
en_US |
dc.subject |
Machine learning |
en_US |
dc.subject |
Intrusion detection |
en_US |
dc.title |
Anomaly based intrusion detection for a biometric identification system using neural networks |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Mgabile, T., Msiza, I., & Dube, E. (2012). Anomaly based intrusion detection for a biometric identification system using neural networks. Planetary Scientific Research Center (PSRC). http://hdl.handle.net/10204/6196 |
en_ZA |
dc.identifier.chicagocitation |
Mgabile, T, IS Msiza, and E Dube. "Anomaly based intrusion detection for a biometric identification system using neural networks." (2012): http://hdl.handle.net/10204/6196 |
en_ZA |
dc.identifier.vancouvercitation |
Mgabile T, Msiza I, Dube E, Anomaly based intrusion detection for a biometric identification system using neural networks; Planetary Scientific Research Center (PSRC); 2012. http://hdl.handle.net/10204/6196 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Mgabile, T
AU - Msiza, IS
AU - Dube, E
AB - 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%.
DA - 2012-10
DB - ResearchSpace
DP - CSIR
KW - Biometric systems
KW - Neural network
KW - Machine learning
KW - Intrusion detection
LK - https://researchspace.csir.co.za
PY - 2012
SM - 978-93-82242-09-3
T1 - Anomaly based intrusion detection for a biometric identification system using neural networks
TI - Anomaly based intrusion detection for a biometric identification system using neural networks
UR - http://hdl.handle.net/10204/6196
ER -
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en_ZA |