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In view of the traditional attack detection method mainly anomaly attack behavior rules and network data membership size discrimination, can only detect abnormal for known attacks, to new anomaly attack, low rate of detection algorithm, calculation of large quantities of data. Put forward a new way of distributed network anomaly detection, through the study of the iterative clustering of data in a distributed network classify the normal and abnormal data, data matrix comparison matrix mapping model, preliminary judgment on abnormal attack data. Establish the particle density function in the matrix, through calculating the particle density change abnormal attack probability, finally the data on the weighted and wave filter to determine abnormal data attack characteristics, attack detection model is established. Simulation experiments show that the optimization of distributed network anomaly detection model improves the adaptability of abnormal data attack detection, attack in the network signal interference circumstance, still can accurately detect the small network attack characteristics of abnormal data. Effectively improve the detection accuracy of the distributed network, accelerate the testing speed and stability.