A rule-based training for artificial neural network packet filtering firewall

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dc.contributor.authorKhunkitti, Akharin
dc.contributor.authorChongsujjatham, Ponsuda
dc.date.accessioned2026-08-06T10:26:08Z
dc.date.available2026-08-06T10:26:08Z
dc.date.issued2019-11-01
dc.description.abstractThe Artificial Neural Network has been used in many network applications, including firewalls. Training process of neural network is very important to define the intelligence of the systems. Many artificial neural network firewalls used direct network packets for training process, which may be difficult to get training samples and may not follow their firewall's policies. This research work proposes a rule-based training for artificial neural network packet filtering firewall. The developed neural network model is trained by generating samples from legacy firewall ruleset. Each rule has been converted to random training samples. All firewall's rules are used to generate the training sample data, rule by rule. The accuracy results show high accuracy with some behavior studies. The number of samples per rule, number of rules and rule style, including default rule and rule-scope effects, have been studied for the best accuracy results. This study also concludes the styles of firewall ruleset for the best accuracy of the proposed system.
dc.identifier.citation2019 6th International Conference on Systems and Informatics Icsai 2019, 1010-1014, 2019
dc.identifier.doi10.1109/ICSAI48974.2019.9010431
dc.identifier.other2-s2.0-85081967765
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/10302
dc.source2019 6th International Conference on Systems and Informatics Icsai 2019
dc.subjectAI
dc.subjectANN
dc.subjectArtificial Neural Network
dc.subjectComponent
dc.subjectPacket Filtering Firewall
dc.subjectRule-Based Training
dc.titleA rule-based training for artificial neural network packet filtering firewall
dc.typeConference Paper

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