An integrated optimization model of network behavior victimization identification based on association rule feature extraction

An integrated optimization model of network behavior victimization identification based on association rule feature extraction

Blog Article

The identification of the risk of network behavior victimization was of great significance for the prevention and warning of telecom network fraud.Insufficient mining of network behavior features and difficulty in determining relationships, an integrated optimization model for network behavior weboost splitter victimization identification based on association rule feature extraction was proposed.The interactive traffic data packets generated when users accessed websites were captured by the model, and the implicit and explicit behavior features in network traffic were extracted.Then, the association rules between features were mined, and the feature sequences were reconstructed using the FP-Growth algorithm.

Finally, an analysis model of telecom network fraud victimization based on network traffic analysis was established, combined with the telemarkskongen flue stochastic forest algorithm of particle swarm optimization.The experiments show that compared with general binary classification models, the proposed model has better precision and recall rates and can effectively improve the accuracy of network fraud victimization identification.