Based on the self-organizing map network clustering algorithm, an HTTP tunnel attack anomaly detection model is proposed. The characteristics of HTTP connection samples, step-by-step optimization training of SOM network, and the balance between false positive and false negative rates are discussed. The model is implemented and the detection results are verified. The results show that the system can better identify normal HTTP connections and HTTP tunnel connections, and the false positive/false positive rate reaches the best balance.
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