Country-Movement Time Learning for Geographic Impossibility Detection

The patent extracts and removes spoofing signals from the training data before using it to create the learning model. By separating legitimate position update requests from malicious spoofing attempts, the system maintains detection capability while improving accuracy. The collection apparatus filters out abnormal patterns that indicate spoofing, ensuring only clean data is used for model training.

The patent converts the harmful spoofing signals into beneficial detection training by analyzing their patterns to identify and filter them out. The system uses the presence of spoofing signals as a teaching moment to improve the learning model's ability to distinguish between legitimate and malicious requests, ultimately enhancing detection accuracy.

The learning model performs self-learning by automatically analyzing training data and determining statistical movement times between countries without requiring manual confirmation. The system processes position update requests, calculates movement durations, and builds its own knowledge base, eliminating the need for human intervention while maintaining high detection accuracy.

The system performs preliminary learning and data processing in advance, where the learning model pre-calculates statistical movement times and stores them for future detection. This preliminary action eliminates the need for manual confirmation during actual detection operations, making the system both accurate and operationally simple.