Frame Synchronization Waveform Checks for MITM Spoofing Detection
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Solution Overview
Problem
Ranging and localization systems using narrow-band radios like Bluetooth are vulnerable to man-in-the-middle (MITM) attacks, which manipulate timing to spoof proximity, compromising security in applications such as secure keyless entry.
Innovation Solution
Implement frame synchronization detection using over-sampling and comparison with a reference waveform to identify deviations in the frame synchronization pattern, detecting MITM attacks by analyzing distortions in the waveform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frame synchronization detection is performed using conventional methods, then the system can detect legitimate signals, but it cannot distinguish between legitimate and spoofed signals, making it vulnerable to MITM attacks
Solution Approach 1:
The system pre-generates a reference waveform that represents the expected frame synchronization pattern before receiving actual signals. This reference waveform is stored and used for comparison with received signals, enabling the system to detect deviations caused by MITM attacks without complex real-time analysis
Solution Approach 2:
The patent replaces conventional frame synchronization detection methods with waveform comparison analysis. Instead of relying on traditional correlation-based detection, the system compares the actual received waveform against a pre-generated reference waveform, analyzing deviations in the waveform structure to identify spoofed signals
2Ease of operation
If the system uses simple timing-based distance estimation, then the operation is simple and fast, but it is vulnerable to timing manipulation by intruders
Solution Approach 1:
The system introduces waveform analysis as an intermediary verification step between signal reception and distance estimation. Before accepting timing-based distance measurements, the system analyzes the waveform structure to ensure it matches the expected pattern, preventing intruders from manipulating timing without being detected
Solution Approach 2:
The system performs preliminary waveform verification before accepting distance measurements. By checking whether the received signal's waveform matches the expected pattern in advance, the system prevents timing manipulation attacks from succeeding, maintaining both simplicity and security
3Reliability
If the system performs detailed waveform analysis to detect spoofing, then security is improved, but processing time and computational resources increase
Solution Approach 1:
The reference waveform is generated and stored in advance, containing all the characteristics needed for spoofing detection. This eliminates the need for complex real-time waveform generation and analysis, allowing the system to perform quick comparisons between received signals and the pre-prepared reference, maintaining high security with minimal processing time
Solution Approach 2:
The system creates a precise copy of the expected frame synchronization waveform as a reference template. This reference copy is then used for rapid comparison with received signals, enabling detailed waveform analysis without the computational burden of generating or complexly analyzing waveforms in real-time
Data Source
AI summary
Techniques are described to improve the security of frame synchronization detection between wireless devices in high accuracy positioning (HAP) applications using personal area networks (PANs). A receiver may detect whether a frame synchronization pattern has been manipulated by comparing the sampled data of the received frame synchronization pattern with a reference waveform predicted as the frame synchronization pattern. The receiver may reuse the data in the correlation buffer at the moment a correlator finds a peak and declares that the synchronization pattern is found. The correlator may also provide fractional timing information associated with the correlation peak for the receiver to create a delayed reference phase differential pattern. The receiver may subtract the data in the correlation buffer by the delayed reference differential data and look for absolute deviations in the output of such subtraction that exceed a predetermined threshold. Specific patterns or signatures of error may also be analyzed.