Intrusion Detection is one of the most effective and widely used implementation against the attacks and threats .Further more attackers keeps on varying their attacking techniques and tools .In this paper we have tried to perform a simulation study to evaluate the performance of varied machine learning classifiers to detect intrusion detection based on KDD 99 cups data set [1] focusing on enhancing the proficiency of Intrusion Detection system (IDS).

DoS- Denial Of Service; U2R-User to root; R2L: Root to local; CIA-Confidentilaity,Integrity,availability ; CM-Confusion Matrix; MLP-Multi Layer perceptron ; NEA-Nearest Cluster Algorithm.; GAU:- Gaussian; K-M: K means algorithm; CPE: - Cost Per example; Pd:Probabilty Detection; FaR:False alrm Rate

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