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Intrusion Detection System (IDS) is widely used to verify the incoming traffic whether it is malicious or benign connection, but traditional IDS requires a lot of human efforts and costs vast amount of computational overhead to build the set of rules in order to distinguish the intruders connection (from suspicious traffic). In view of this limitation, many researchers are adopting and researching the potential data mining and machine learning techniques to assist the stated tasks in a quicker and semi-automated manner. One of the popular statistical models would be the decision tree. It builds a simpler and straightforward tree model based on existing pre-classified network traffic database.
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