Intrusion detection has been the major necessities of the current information rich computing environment. Major challenges facing intrusion detection systems include the huge size of data to be analyzed and the ever -changing attack types. In order to enforce high protection levels against threats, a number of software tools are currently developed. In this paper, two grains levels intrusion detection system (IDS) is suggested (fine-grained and coarse-grained). In normal case, where intrusions are not detected, the most suitable IDS level is the coarse-grained to increase IDS performance. As soon as any intrusion is detected by coarse-grained IDS, the fine-grained is activated to detect the possible attack details. Very fast decision tree algorithm is used in both of these detection levels. Experimental results demonstrate that the proposed model is highly successful in detecting known and unknown attacks, and can be successfully adapted with packets' flow to increase IDS performance.
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Network security; Intrusion detection system; Classification; Very fast decision tree algorithm, Manet, Mac