| _version_ |
1860796916683505664
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| building |
INTELEK Repository
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| collection |
Online Access
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| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2021-03-14 02:54:24
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| eventvenue |
Penang, Malaysia
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| format |
Restricted Document
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| id |
10674
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| institution |
UniSZA
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| originalfilename |
4752-01-FH03-FIK-21-51439.pdf
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| person |
Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML
like Gecko) Chrome/88.0.4324.190 Safari/537.36
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| recordtype |
oai_dc
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| resourceurl |
https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10674
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| spelling |
10674 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10674 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Conference Conference Paper application/pdf 8 1.6 Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML like Gecko) Chrome/88.0.4324.190 Safari/537.36 Skia/PDF m88 2021-03-14 02:54:24 4752-01-FH03-FIK-21-51439.pdf UniSZA Private Access Metaheuristic based ids using multi-objective wrapper feature selection and neural network classification Due to the significant ongoing expansion of computer networks in our lives nowadays, the demand for network security and protection from cyber-attacks has never been more imperative to either clients or businesses alike, which signifies the key role of cyber intrusion detection systems in network security. This article proposes a cyber-intrusion detecting system classification with MLP trained by a hybrid metaheuristic algorithm and feature selection based on multi-objective wrapper method. The classifier, named as HADMLP is trained using a hybridization of the artificial bee colony along with the dragonfly algorithm. A multi-objective artificial bee colony model which is wrapper-based is used for selection of feature. Hence, collective name of the proposed technique referred as MO-HADMLP. For performance evaluation, the proposed method was assessed using ISCX 2012 and KDD CUP 99 datasets. The results of our experiments indicate a significant enhancement to the efficacy of network intrusion detection when compared to other approaches. 2nd International Conference on Advances in Cyber Security Penang, Malaysia
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| spellingShingle |
Metaheuristic based ids using multi-objective wrapper feature selection and neural network classification
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| summary |
Due to the significant ongoing expansion of computer networks in our lives nowadays, the demand for network security and protection from cyber-attacks has never been more imperative to either clients or businesses alike, which signifies the key role of cyber intrusion detection systems in network security. This article proposes a cyber-intrusion detecting system classification with MLP trained by a hybrid metaheuristic algorithm and feature selection based on multi-objective wrapper method. The classifier, named as HADMLP is trained using a hybridization of the artificial bee colony along with the dragonfly algorithm. A multi-objective artificial bee colony model which is wrapper-based is used for selection of feature. Hence, collective name of the proposed technique referred as MO-HADMLP. For performance evaluation, the proposed method was assessed using ISCX 2012 and KDD CUP 99 datasets. The results of our experiments indicate a significant enhancement to the efficacy of network intrusion detection when compared to other approaches.
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| title |
Metaheuristic based ids using multi-objective wrapper feature selection and neural network classification
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| title_full |
Metaheuristic based ids using multi-objective wrapper feature selection and neural network classification
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| title_fullStr |
Metaheuristic based ids using multi-objective wrapper feature selection and neural network classification
|
| title_full_unstemmed |
Metaheuristic based ids using multi-objective wrapper feature selection and neural network classification
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| title_short |
Metaheuristic based ids using multi-objective wrapper feature selection and neural network classification
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| title_sort |
metaheuristic based ids using multi-objective wrapper feature selection and neural network classification
|