Machine Identification via Manufacturing Variability
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Solution Overview
Problem
Current methods for uniquely identifying machines used in online transactions are not sufficiently precise, leading to potential false positives and are vulnerable to fraud due to limitations in time difference measurements and token-based approaches, and do not provide a unique identifier for machines with similar configurations.
Innovation Solution
The method utilizes the inherent variability in manufacturing processes by measuring the runtime of a standard piece of code on a machine to generate a unique machine effective speed calibration (MESC), which can be used to identify and match machines across sessions, and stores this information in a machine identity history file for security risk analysis and fraud prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If time difference measurement is used to identify machines, then machine identification can be achieved, but the precision is insufficient leading to false positives
Solution Approach 1:
The patent changes the identification parameter from time difference (coarse granularity) to manufacturing variability characteristics (fine granularity). By measuring subtle variations in hardware components' manufacturing processes, the system achieves higher precision machine identification without increasing false positives, as these physical variations are unique to each machine.
Solution Approach 2:
The patent creates a digital copy of the machine's manufacturing variability characteristics by measuring and storing baseline values of hardware parameters. This digital fingerprint serves as a unique identifier that can be compared against subsequent measurements to reliably identify the same machine across different sessions.
2Measurement precision
If token storage on machine is used, then machine identification is possible, but tokens can be stolen and moved to another machine
Solution Approach 1:
The patent extracts the identification mechanism from software-based tokens stored on the machine and moves it to hardware-based manufacturing variability characteristics. By measuring physical properties of hardware components that are inherent to the machine's construction, the system creates an identifier that cannot be copied or transferred, eliminating the fraud vulnerability of stolen tokens.
Solution Approach 2:
The patent converts the previously harmful effect of manufacturing variability (which caused inconsistencies in hardware performance) into a beneficial feature for identification. The unique physical variations in each machine's components, once considered defects, are now exploited as the foundation for creating unforgeable machine fingerprints.
3Loss of information
If software installation is used to collect machine information, then detailed machine properties can be obtained, but many users block software downloads
Solution Approach 1:
The patent enables the machine to self-reveal its identification characteristics without requiring external software installation. By leveraging naturally occurring manufacturing variations in hardware components, the system allows machines to automatically provide their unique fingerprints through passive measurement of existing hardware behavior, eliminating the need for user-perceived intrusive software downloads.
Solution Approach 2:
The patent replaces the mechanical approach of installing software agents on machines with a remote measurement approach. Instead of deploying physical software components that users must accept, the system uses network-based measurements of hardware characteristics to collect identification information, substituting one system with a less intrusive alternative.
4Measurement precision
If standard machine configuration information is used, then identification can be performed, but machines with similar configurations cannot be distinguished
Solution Approach 1:
The patent applies local quality by focusing on specific localized variations in hardware components rather than overall machine configuration. By measuring subtle manufacturing differences in individual components (such as processor cache timing, memory access patterns, or disk sector variations), the system identifies unique characteristics at the component level that distinguish otherwise identical machines.
Data Source
AI summary
A method and system for identifying a machine used for an online session with an online provider includes executing a lightweight fingerprint code from a provider interface during an online session to collect and transmit machine and session information; generating and storing a machine signature or identity including a machine effective speed calibration (MESC) which may be used to identify the machine when the machine is used in a subsequent online session by a method of matching the machine signature and MESC to a database of machine identities, analyzing a history of the machine's online sessions to identify one or more response indicators, such as fraud indicators, and executing one or more responses to the response indicators, such as disabling a password or denying an online transaction, where the response and response indicator may be provider-designated.