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. Many machine-learning methods 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 and are most likely to be applied for the IDS in the real network. The machine learning model based on this 28 features subset achieves 99.9% accuracy for both two-class and multiclass classification. From our experiments using the IDS, we have developed good performance in detecting attacks on real networks.
|Number of pages||10|
|Journal||International Journal of Communication Networks and Information Security|
|Publication status||Published - 1 Jan 2018|
- Feature selection
- Intrusion detection system
- Model selection