Sensor Validator v3.5: Adaptive Multi-Modal Sensor Validation and Threat Detection Framework
Sensor Validator v3.5 is a Python-based framework for adaptive validation of environmental and chemical sensor systems. The platform combines multi-modal feature extraction, anomaly detection, machine-learning classification, drift monitoring, automatic recalibration, hardware abstraction, and synthetic validation within a single deployable workflow. The framework supports both high-rate and low-rate sensor modalities and employs a two-stage machine-learning pipeline consisting of an Isolation Forest anomaly detector and a Random Forest threat classifier. Sensor health is continuously monitored using a drift warning system that provides GREEN, AMBER, RED, and BLACK operational status levels and supports automated recalibration when performance degradation is detected. The software includes: Multi-modal feature extraction from sensor arrays and single-channel sensors. Isolation Forest anomaly detection. Random Forest threat classification. Statistical drift monitoring and alerting. Adaptive recalibration functions. Hardware abstraction layer supporting multiple sensor architectures. Synthetic signal generation for validation and testing. Demonstration workflows suitable for Google Colab deployment. The framework is intended as a research and development platform for sensor validation, environmental monitoring, chemical sensing, biosensing, and machine-learning-assisted threat detection applications.