Abstract
Intrusion Detection System (IDS) has increasingly become a crucial issue for computer and network systems. Optimizing performance of IDS becomes an important open problem which receives more and more attention from the research community. This paper, design and develop a proposed multi-layer intrusion detection model to achieve high efficiency and improve the detection and classification rate accuracy. Also the proposed model was improved the detection rate for known and unknown attacks by training the hybrid model on the known intrusion data. Then the model applied for unknown attacks by introducing new types of attacks that are never seen by the training module. The experimental results showed that the proposed multi-layer model using C5 decision tree achieves higher classification rate accuracy, and less false alarm rate.
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