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The growing dependence of modern society on telecommunication and information networks has become inevitable. Therefore, the security aspects of such networks play a strategic role in ensuring protection of data against misuse. Intrusion Detection systems (IDS) are meant to detect intruders who elude the "First line" protection. Data mining techniques are being used for building effective IDS. In this paper the authors analyze the performance of some data classifiers in a heterogeneous environment using voting ensemble system with the purpose of detecting anomaly based network intrusions. Experimental results using KDDCup 1999 benchmark dataset demonstrate that the voting ensemble technique yield significantly better results in detecting intrusions when compared to other techniques.
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