Adaptive Layered Approach using C5.0 Decision Tree for Intrusion Detection Systems (ALIDS)
نویسنده
چکیده
Intrusion Detection System (IDS) is one of a crucial issue and a major research problem in network security. This work, An Adaptive multi-Layer Intrusion Detection System (ALIDS) is designed and developed to achieve high efficiency, scalability, flexibility and improve the detection and classification rate accuracy. We apply C5 decision tree on our model. Our experimental results showed that the proposed ALIDS model with different order of training classes enhances the accuracy of U2R and R2L. Keywords-component; network intrusion detection; Decision Tree.
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