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...
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| Main Authors: | , , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://eprints.unisza.edu.my/4752/1/FH03-FIK-21-51439.pdf http://eprints.unisza.edu.my/4752/ |
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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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