UWB Ranging Plausibility Checks Against Relay Attacks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing Ultra-WideBand (UWB) transactions are vulnerable to relay and Man-In-The-Middle attacks, which compromise the security of short-range communications between IoT devices by allowing unauthorized access.
Innovation Solution
A method to assess the plausibility of ranging measurements using unsupervised or semi-supervised Machine Learning algorithms, such as Grid Clustering or one-class SVM, to analyze UWB metrics like received power, channel impulse response, and time of flight, identifying outliers that indicate malicious or malfunctioning devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If UWB ranging measurements are used for authentication, then short-range communication security is improved, but the system becomes vulnerable to relay and Man-In-The-Middle attacks
Solution Approach 1:
The patent changes the parameter being measured from simple time-of-flight to a composite assessment including received power levels, channel impulse response characteristics, and timing measurements. By analyzing multiple parameters simultaneously and assessing their plausibility relationships, the system can detect relay attacks where the physical layer characteristics don't match the reported ranging distance
Solution Approach 2:
The system implements feedback by continuously monitoring UWB transaction metrics and using machine learning algorithms to assess whether measured values are plausible. The system learns from normal transaction patterns and provides feedback when anomalies are detected, allowing dynamic adjustment of security responses based on real-time assessment of ranging measurement validity
2Reliability
If multiple UWB metrics are collected and analyzed using machine learning, then detection of malicious devices is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent applies partial action by collecting a subset of relevant UWB metrics (received power, channel impulse response, timing measurements) rather than all possible parameters. The machine learning assessment focuses on the most discriminative features for detecting relay attacks, avoiding the computational overhead of analyzing excessive data while maintaining high detection accuracy
Solution Approach 2:
The system uses unsupervised or semi-supervised machine learning algorithms that can autonomously learn normal transaction patterns and detect anomalies without requiring extensive manual training or configuration. The algorithms self-adapt to the specific UWB environment and device characteristics, reducing the need for complex external computational resources
Data Source
AI summary
Systems and methods of assessing a plausibility of a ranging measurement are provided. In some embodiments, a method of assessing a plausibility of a ranging measurement includes: obtaining the ranging measurement from a remote device; obtaining one or more measurements associated with the ranging measurement; and based on the one or more measurements associated with the ranging measurement, determining the plausibility of the ranging measurement. The embodiments disclosed herein determine the reliability of the measured range and thus enforce the security 10 level of Ultra-WideBand (UWB) transactions to be secured. Some embodiments are based on existing and standardized metrics. Some embodiments include a capability to auto-assess whether it is reliable to estimate the plausibility of the transaction range. In some embodiments, the computations needed are relatively simple and can be performed by relatively simple devices.