A new feature selection model based on ID3 and bees algorithm for intrusion detection system
作者： Adel Sabry EESA , Zeynep ORMAN , Adnan Mohsin Abdulazeez BRIFCANI
DOI: 10.3906/ELK-1302-53
关键词: Constant false alarm rate 、 Pattern recognition 、 Data mining 、 Classifier (UML) 、 Information security 、 Feature selection 、 Intrusion detection system 、 ID3 algorithm 、 ID3 、 Artificial intelligence 、 Computer science 、 Bees algorithm
摘要: Intrusion detection systems (IDSs) have become a necessary component of computers and information security framework. IDSs commonly deal with large amount data traffic these may contain redundant unimportant features. Choosing the best quality features that represent all exclude is crucial topic in IDSs. In this paper, new combination approach based on ID3 algorithm bees (BA) proposed to select optimal subset for an IDS. The BA used generate features, as classifier. model applied KDD Cup 99 dataset. obtained results show feature generated by ID3-BA gives higher accuracy rate lower false alarm when compared using
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