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Network intrusion detection is a way to separate normal behaviors from the attacked ones. An Intrusion Detection System (IDS) gathers and analyzes information from various areas within a computer or a network to identify possible security breaches, which include both intrusions which are attacks from outside the organization and misuse which are attacks from within the organization. The proposed system is based on the adaboost algorithm with Naive Bayes classifier to detect network intrusions with high detection rates and low false-alarm rates. This results in low computational complexity and error rates. Naive Bayes classifier is used as a weak classifier. The objective of this system is to reduce the false alarm rate and to increase the attack detection rate.
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