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In this paper, we introduce an Intrusion Detection system (IDS) based Hybrid Evolutionary Neural Network (HENN). A brief overview of IDS, genetic algorithm, and related detection techniques are discussed. The system architecture is also introduced. Factors affecting the genetic algorithm are addressed in detail. Unlike other implementations of IDS, Input features, network structure and connection weights are evolved using genetic algorithm in HENN. This is helpful for identification of complex anomalous behaviors. Experimental results show that the proposed IDS can efficiently improve the detection rate and correctness rate.