Degenerate Component for Hardware Trojan Detection in SDR
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Solution Overview
Problem
The avionics industry faces challenges in ensuring the trustworthiness of software defined radio (SDR) systems due to the complexity and size of digital circuits, making it difficult to detect and mitigate hardware trojans, which can compromise system functionality and pose security risks.
Innovation Solution
Implementing an artificial neural network (ANN) to create a degenerate component that is structurally different but functionally identical to the original, allowing for real-time detection and replacement of malfunctioning components, thereby mitigating hardware trojan threats and ensuring system reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital circuits are made smaller and more complex to increase functionality, then system capability is improved, but trustworthiness and fault detectability deteriorate
Solution Approach 1:
The patent creates a degenerate component that is a functional copy of the original component but with different internal structure. This copy is trained to produce identical outputs for identical inputs, allowing it to serve as a reliable substitute when the original component fails or is compromised by hardware trojans
Solution Approach 2:
The degenerate component is trained in advance on buffered input and output samples to learn the functional behavior of the original component before any fault occurs. This preliminary training enables the degenerate component to immediately take over when needed without requiring real-time analysis of component behavior
2Difficulty of detecting and measuring
If traditional detection methods like logic testing and reverse engineering are used to detect hardware trojans, then detection capability is improved, but time consumption and error rate increase
Solution Approach 1:
The patent replaces traditional mechanical and manual detection methods (logic testing, reverse engineering) with an artificial neural network-based system. The ANN automatically learns component behavior patterns and detects faults through functional comparison, eliminating the need for time-consuming manual analysis and reducing detection errors
3Reliability
If redundant circuits are used to ensure system reliability, then system reliability is improved, but device complexity and resource usage increase
Solution Approach 1:
Instead of creating full redundant circuits, the patent uses a degenerate component that shares the same functional interface but has different internal structure. This approach provides reliability through functional equivalence rather than structural duplication, reducing the complexity overhead compared to traditional redundancy methods
Solution Approach 2:
The degenerate component serves multiple functions: it acts as a normal component during operation, serves as a backup when the original fails, and can be used for fault detection by comparing outputs. This multi-functionality reduces the need for separate redundant systems
Data Source
AI summary
Systems and methods disclosed herein provide training an artificial neural network (ANN) on buffered input and output samples of an original component within a system such that the ANN is configured to produce a degenerate component, the degenerate component configured to generate the same outputs as the original component; comparing the outputs from the original component to outputs of the degenerate component during actual component operation; and in the event of a failure of the original component, replacing the original component with the degenerate component.