Sensor-Host Pairing Validation via Behavioral Modeling
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Solution Overview
Problem
Existing electronic sensor-host pairing methods lack continuous validation and authentication, leading to potential separation of sensors from their hosts due to accidental or malicious reasons, such as theft or loss, without adequate verification between authentication checkpoints.
Innovation Solution
A method involving the definition of sensor-host pairing and context metadata, where sensor spatiotemporal sighting events and application events are recorded and analyzed using spatiotemporal tracking logic and machine learning algorithms to construct a behavioral model, identifying inconsistent behavior and generating alerts for separation or misalignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous monitoring and validation of sensor-host pairs is implemented, then security and reliability are improved, but system complexity and computational resources increase
Solution Approach 1:
The system automatically monitors, validates, and alerts on sensor-host separations without requiring manual intervention. The behavioral model self-updates using collected data, and the alert system autonomously notifies relevant parties when separations occur, reducing the need for complex manual monitoring procedures.
Solution Approach 2:
The system continuously collects data on sensor sightings and host locations, compares this data against the behavioral model, and uses the results to validate or update the model. This feedback loop enables the system to adapt to changing patterns while maintaining reliable sensor-host pairing detection.
2Loss of time
If real-time tracking and validation of sensor-host pairs is implemented, then response time to separation events is improved, but energy consumption increases
Solution Approach 1:
The system performs periodic validation checks by comparing current sightings against the behavioral model at scheduled intervals rather than continuously processing all data in real-time. This periodic approach maintains timely detection of separations while reducing overall computational energy consumption.
Solution Approach 2:
The behavioral model is pre-trained using historical data to establish normal patterns of sensor-host interaction. This preliminary modeling allows the system to quickly identify deviations from normal behavior without requiring complex real-time analysis, thus reducing processing energy requirements while maintaining fast response to separation events.
Data Source
AI summary
A method for operating a system to continuously validating and authenticating a host and sensor pair involves defining a sensor-host pairing and context metadata. The system receives a plurality of sensor spatiotemporal sighting events of a sensor-host pair from a plurality of collection devices. The system records the identifier, signal strength, location and time of the sighting events as sensor spatiotemporal sighting events in a controlled memory data structure. The system collects a plurality of application events related to each sensor-host pair and stores the events in the controlled memory data structure. The system constructs a behavioral model from the sensor spatiotemporal sighting events through operation of spatiotemporal tracking logic. The system receives real time sighting events and compares it to the behavioral model to identify inconsistent behavior through operation of a behavior comparator.