Anomaly Traffic Identification and Defense Model in Networks Based on the Multi-Gate Mixture of Experts
This paper proposed a big data network anomaly traffic identification and defense strategy generation model based on the multi-gate mixture of experts(MMoE)model.This model is particularly suitable for scenarios involving mixed attack traffic during peak business periods.First,the MMoE model conducted real-time monitoring and anomaly identification of network traffic,distinguishing between normal traffic peaks caused by business demands and genuine anomalous traffic,effectively reducing false alarms.When anomalous traffic was detected,the system used it as input to generate targeted defense strategies.Secondly,the MMoE model coordinated the expert models for anomaly detection and defense strategy generation,enhancing the precision of identification and the effectiveness of strategy generation.Experimental results on datasets obtained from real business scenarios show that the identification accuracy and defense effect of the model proposed in this study are better than mainstream machine learning models and can accurately identify abnormal attack traffic mixed during business peaks and generate appropriate defense strategies.
anomaly traffic identificationdefense strategy generationmixture of experts modelstealth attack
NETL
NSTL
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