Decentralized Trust Assessment for Aircraft Data Integrity
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Solution Overview
Problem
Conventional avionics systems rely solely on trust assessment modules, which are limited in detecting specific conditions and cannot verify the authenticity and content of data streams, leading to potential system failures and lack of robustness.
Innovation Solution
A decentralized trust assessment system combining trust modules with neural networks to verify the quality, authenticity, and content of data streams, allowing for pattern detection without specific preprogramming, thereby enhancing the reliability of aircraft subsystems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only a trust assessment module is used, then the system structure is simple, but the system robustness and detection capability are limited
Solution Approach 1:
The patent combines a trust assessment module with a neural network to create a hybrid system. The trust module provides rule-based detection for known failure modes, while the neural network provides pattern recognition for complex, multi-parameter anomalies. This merging allows the system to achieve higher reliability through multiple detection mechanisms without requiring complete redesign of the avionics architecture.
Solution Approach 2:
The neural network component is designed to perform multiple detection functions simultaneously - it can identify sensor failures, spoofing attempts, and abnormal operational patterns across different aircraft systems. This multi-functionality allows a single added component to provide broad detection coverage, improving system robustness without proportionally increasing complexity.
2Measurement precision
If a trust module is programmed to detect specific conditions, then detection accuracy for those conditions improves, but the module cannot detect unprogrammed conditions
Solution Approach 1:
The patent merges the trust module's precise rule-based detection with the neural network's adaptive pattern recognition. The trust module maintains high detection accuracy for known failure modes through programmed rules, while the neural network compensates for limited coverage by learning to detect novel and complex patterns from training data, providing both precision and versatility.
Solution Approach 2:
The neural network acts as an intermediary that bridges the gap between specific rule-based detection and general anomaly detection. It processes data streams and identifies patterns that indicate potential issues, then triggers the trust module's detailed analysis for confirmed anomalies, creating a layered detection approach that achieves both accuracy and broad coverage.
3Reliability
If the trust module looks for failed, stuck, or extreme data streams, then common failures are detected, but sophisticated spoofing and patterns remain undetected
Solution Approach 1:
The patent introduces dynamic pattern recognition through the neural network that adapts to detect sophisticated spoofing attempts. While the trust module handles static, predefined failure conditions, the neural network dynamically analyzes multi-parameter patterns and temporal relationships in data streams, enabling detection of adaptive spoofing techniques that change over time or present as plausible but incorrect patterns.
Solution Approach 2:
The patent replaces the mechanical, rule-based detection approach with an intelligent system using neural networks. This substitution enables the system to detect complex spoofing patterns that cannot be captured by simple threshold checks or predefined rules, as the neural network can identify subtle correlations and anomalies across multiple data streams that indicate sophisticated attacks.
Data Source
AI summary
A decentralized trust assessment system, comprising a neural network, a trust module, and a local subsystem, wherein the trust module controls whether a plurality of inputs to the local subsystem are trustworthy. The decentralized trust assessment system provides rotorcraft and tiltrotor aircraft with airborne systems able to detect bad and spoofed data from a wide variety of data streams.