Machine-Learned Device Orientation Fingerprints for User Verification
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Solution Overview
Problem
Existing cybersecurity systems face challenges in accurately identifying anomalous device usage that may indicate malicious activity, particularly in distinguishing between legitimate users and potential bad actors, with a high false positive rate.
Innovation Solution
Implementing device orientation fingerprints using a machine learning model that tracks and analyzes the orientation, identity, and activity data of user devices to create a unique fingerprint, which can verify if the known user is currently using the device by comparing current orientation and activity data against a baseline model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional user behavioral analytics are used to monitor user behavior and identify anomalous activity, then threat detection capability is improved, but false positive rate increases
Solution Approach 1:
The patent segments user behavior analysis into multiple independent components: device orientation fingerprinting, user activity tracking, and anomaly detection. By dividing the complex analysis into separate modules that each handle specific aspects of behavior, the system reduces false positives while maintaining comprehensive threat detection capability.
Solution Approach 2:
The patent introduces device orientation as a new dimension for user behavior analysis. Instead of relying solely on traditional behavioral metrics, the system incorporates spatial orientation data to create a multi-dimensional fingerprint that more accurately distinguishes between legitimate users and malicious actors, thereby reducing false positives.
2Reliability
If continuous monitoring of user behavior is implemented to build baseline models, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-computing device orientation fingerprints during device setup or first use. This baseline fingerprint is stored and used for subsequent authentication decisions, eliminating the need for continuous complex analysis during normal operation and reducing system complexity while maintaining high detection accuracy.
Solution Approach 2:
The system performs self-service by automatically generating and updating user behavior baselines without requiring manual configuration or complex user input. The device orientation fingerprinting operates autonomously to establish and maintain detection models, reducing the operational complexity burden on users and administrators.
3Reliability
If device orientation fingerprinting is implemented to verify user identity, then security verification accuracy is improved, but data collection requirements increase
Solution Approach 1:
The patent extracts and focuses on a specific subset of device characteristics - namely orientation data - to create the fingerprint. By selecting only the most discriminating and relevant data elements rather than collecting all possible device metrics, the system achieves high verification accuracy while minimizing data collection requirements.
Solution Approach 2:
The patent changes the parameter being measured from traditional behavioral metrics to device orientation parameters. This parameter transformation enables more accurate verification with less data, as orientation characteristics are inherently more distinctive and harder to spoof than traditional behavioral patterns, reducing the quantity of data needed for reliable verification.
Data Source
AI summary
Creating and using device orientation fingerprints can include detecting a request to create an orientation fingerprint for a user device, where the orientation fingerprint defines an orientation of the user device and includes a machine learning model that models, for the user device and a known user of the user device, multiple orientations of the device for multiple activities. Operational data that includes orientation data, identity data, and activity data can be obtained. The operational data can be provided to machine learning to output the orientation fingerprint and the orientation fingerprint can be stored with data that associates the orientation fingerprint with the known user.