Enterprise Sensor Normalization for Context-Aware Threat Detection

The patent introduces a normalization layer that acts as an intermediary between diverse sensor inputs and the analysis engine. This layer standardizes data from multiple vendors into a uniform format, enabling integration without directly complicating the core analysis system. The normalization layer handles format conversion and data harmonization, isolating complexity from the main protection logic.

The system implements a universal data format that can accommodate sensor data from multiple vendors and sensor types. This universal format serves as a common interface that simplifies integration while maintaining the ability to handle diverse input sources. The normalization process maps various vendor-specific formats to this universal structure, achieving versatility without proportional increases in complexity.

The system performs preliminary machine learning model training during off-peak periods or using historical data, so that the model is ready for deployment without requiring continuous heavy computation. The normalization of sensor data and feature extraction are performed in advance, reducing the computational burden during real-time operation. This allows self-learning capabilities to be maintained while managing energy consumption during active monitoring.

The system applies machine learning selectively to the most critical sensor data and thresholds rather than continuously processing all sensor inputs at full computational capacity. The normalization process identifies and focuses computational resources on the most relevant features and anomalies, performing partial analysis that achieves effective self-learning with reduced energy consumption compared to exhaustive processing of all data.