MGA-IDS: Optimal feature subset selection for anomaly detection framework on in-vehicle networks-CAN bus based on genetic algorithm and intrusion detection approach. | D2R Server publishing the DBLP Bibliography Database, hosted at L3S Research Center

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dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Dogukan_Aksu>
dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Muhammed_Ali_Aydin>
foaf:homepage	<http://dx.doi.org/doi.org%2F10.1016%2Fj.cose.2022.102717>
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dcterms:issued	2022 (xsd:gYear)
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rdfs:label	MGA-IDS: Optimal feature subset selection for anomaly detection framework on in-vehicle networks-CAN bus based on genetic algorithm and intrusion detection approach. (xsd:string)
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swrc:pages	102717 (xsd:string)
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dc:title	MGA-IDS: Optimal feature subset selection for anomaly detection framework on in-vehicle networks-CAN bus based on genetic algorithm and intrusion detection approach. (xsd:string)
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