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A network intrusion detection model based on artificial immune theory is proposed in this paper. In this model, self patterns and non-self patterns are built upon frequent behaviors sequences, then a simple but efficient algorithm for encoding patterns is proposed. Based on the result of encoding, another algorithm for creating detectors is presented, which integrates a negative selection with the clonal selection. The algorithm performance is analyzed, which shows that this method can shrink each generation scale greatly and create a good niche for patterns evolving.