This paper proposes an intelligent prevention and control framework. Combining SSL correlation analysis and graph convolutional network (GCN), it realizes efficient semantic restoration of HTTPS encrypted traffic and multi-hop behavior identification; designs a streaming computing engine based on Kafka-Flink, which supports millisecond anomaly detection and dynamic model updating; and constructs a group portrait model under the heterogeneous information network, which accurately locates vulnerable nodes to fraud. In addition, federal learning is introduced to optimize the virtual base station positioning algorithm, combined with particle filtering and improved Chan-Taylor parameter optimization, to improve the positioning accuracy in non-line-of-sight environments, and the Deep Reinforcement Learning (DRL) framework is used to achieve dynamic reasoning and adaptive defense of fraudulent intent. The framework provides theoretical support and technical breakthroughs at the algorithmic level for telecom fraud prevention and control.
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