Mabayoje, Modinat AboloreAkintola, Abimbola GaniyatBalogun, Abdullateef OluwagbemigaAyilara, Opeyemi2018-05-102018-05-10201518. Mabayoje M.A., Akintola, A. G., Balogun, A. O & Ayilara, O. (2015): Gain Ratio and Decision Tree Classifier for Intrusion Detection. International Journal of Computer Applications (IJCA). 126(1):56-590123-456010.5120/ijca2015905983https://www.ijcaonline.org/research/volume126/number1/modinat-2015-ijca-905983.pdfhttp://hdl.handle.net/123456789/224With the evident need for accuracy in the performance of intrusion detection system, it is expedient that in addition to the algorithms used, more activities should be carried out to improve accuracy and reduce real time used in detection. This paper reviews how data mining relates to IDS, feature selection and classification. This paper proposes architecture of IDS where GainRatio is used for feature selection and decision tree for classification using NSL-KDD99 dataset, It also includes the evaluation of the performance of the Decision tree on the dataset and also on the reduced dataset.enMachine LearningData MiningKnowledge DiscoveryIntrusion Detection SystemArticle