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As one of the security components in Network Security Monitoring System, Intrusion Detection System (IDS) is implemented by many organizations in their networks to detect and address the impact of network attacks. There are many machine-learning methods that have been widely developed and applied in the IDS. Selection of appropriate methods is necessary to improve the detection accuracy in the application of machine-learning in IDS. In this research we proposed an IDS that we developed based on machine learning approach. We use 28 features subset without content features of  Knowledge Data Discovery (KDD) dataset to build machine learning model. From our analysis and experiment we get 28 features subset of KDD dataset that are most likely to be applied for the IDS in the real network. The machine learning model based on this 28 features subset obtained 99.9% accuracy for both two-class and multiclass classification. From our experiments using the IDS we have developed show good performance in detecting attacks on real networks.

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