Temporal Feature Learning Framework for Multi-dimensional Behavior Anomaly Detection in Digital Transaction Platforms

Abstract

Digital transaction platforms face escalating challenges from sophisticated fraudulent activities that exploit multi-dimensional behavioral patterns across temporal sequences. This research presents a comprehensive temporal feature learning framework designed specifically for detecting anomalous behaviors in complex digital transaction environments. The proposed framework integrates adaptive threshold mechanisms with multi-scale temporal feature extraction, enabling real-time identification of suspicious activities while maintaining low false-positive rates. Through systematic analysis of transaction sequences, user interaction patterns, and network-level behavioral signatures, the framework achieves enhanced detection accuracy across diverse attack vectors including coordinated fraud campaigns and automated malicious account operations. Experimental validation demonstrates superior performance compared to conventional rule-based approaches, with detection precision reaching 94.7% and recall maintaining 91.3% across heterogeneous transaction datasets. The adaptive nature of the framework allows dynamic adjustment to evolving threat landscapes without requiring extensive retraining cycles.

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