A cross-social platform distributed anomaly behavior detection method

With the rapid development of social networks, anomaly behavior detection has become essential for ensuring platform security and enhancing user experience. Traditional anomaly detection methods often rely on centralized data processing, making it difficult to address the challenges of cross-platform collaborative detection and failing to fully leverage the temporal and graph structure information within social networks. To improve anomaly behavior detection’s accuracy and generalization ability, this paper proposes a Cross-Platform-Based Distributed Anomaly Behavior Detection Method, FLAD. Specifically, the method employs the Federated Averaging (FedAvg) algorithm for model aggregation, constructing a decentralized anomaly behavior detection model. This approach avoids the exchange of raw user behavior data and enhances the effectiveness of cross-platform collaborative learning. Moreover, this paper introduces a detection model that combines a sliding window and Graph Convolutional Network (GCN), utilizing temporal data and the graph structure of social networks. By partitioning user behavior data into subgraphs via a sliding window, and employing GCN and Long Short-Term Memory (LSTM) models to learn the evolution patterns of temporal and behavioral data, the method improves the accuracy of anomaly behavior detection. Experimental results show that the proposed method achieves up to a 6.31% increase in F1-score compared to baseline models on the Epinions and Digg datasets. Ablation studies further demonstrate that the unified model, aggregated through federated learning, significantly improves accuracy over the initial global model. In the Epinions dataset, accuracy increased by 20.49%, and in the Digg dataset, accuracy improved by 23.13%.