Moving Entity Tracking With ML Anomaly Detection and Secure Data
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Solution Overview
Problem
Existing tracking technologies, such as AIS and GNSS, are vulnerable to security threats like data tampering, hijacking, and unauthorized access, which compromise the reliability and integrity of tracking moving entities.
Innovation Solution
A tracking system equipped with a tracking device that includes a detection module using a machine learning algorithm, particularly a supervised learning algorithm based on a 1D convolutional neural network, to detect anomalies in position data, coupled with cryptographic methods like quantum key distribution for secure communication, ensuring data integrity and alerting a control station.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AIS and GNSS tracking technologies are used to monitor moving entities, then positioning and tracking capability is improved, but security vulnerability to data tampering and unauthorized access worsens
Solution Approach 1:
The patent introduces an intermediary verification mechanism where multiple independent tracking sources (AIS, GNSS, radar, visual recognition) cross-validate each other's data. This mediator layer detects inconsistencies and prevents unauthorized data manipulation, resolving the contradiction between maintaining positioning accuracy and ensuring data integrity against security threats.
Solution Approach 2:
The system implements continuous feedback loops where tracking data is constantly verified against expected patterns and multiple sources. Anomaly detection algorithms provide real-time feedback when data deviations are detected, allowing the system to identify and reject tampered data while maintaining accurate tracking through validated information streams.
2Reliability
If multiple tracking sources and verification mechanisms are added to improve security, then data integrity is improved, but system complexity worsens
Solution Approach 1:
The patent implements a universal verification platform that handles multiple tracking sources (AIS, GNSS, radar, visual systems) through a single integrated architecture. This multi-functional system performs positioning, verification, anomaly detection, and data fusion through common algorithms and processing pipelines, reducing overall system complexity despite incorporating multiple security layers.
Solution Approach 2:
The verification system operates autonomously using self-service mechanisms where the tracking network automatically cross-validates its own data streams without external intervention. Machine learning models continuously learn from historical data to improve verification accuracy, and the system self-corrects anomalies through automated decision-making, reducing the need for complex manual verification protocols.
Data Source
AI summary
A tracking system for tracking the movement of a moving entity is provided. The tracking system comprises a tracking device configured to move together with the moving entity and provide data associated with the movement of the moving entity, and a detection module configured to, on the basis of the data associated with the movement of the moving entity, perform a detection process to detect an anomaly in the data, the detection process comprising a machine learning algorithm. The tracking system is further configured to, in case the anomaly is detected, issue an alert message to be processed at a control station.