Feedback Data Matching for Spoof-Resistant User Verification
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Solution Overview
Problem
Communication systems face challenges in distinguishing between legitimate user data and falsified data presented by bad actors, leading to potential unauthorized access to network resources and sensitive information.
Innovation Solution
A system utilizing machine learning algorithms to analyze biometric and feedback data, employing probabilistic linkage models like the Fellegi-Sunter model, to identify and filter out bad actors by analyzing access patterns and assigning weighted values to datapoints, thereby reducing processor and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional verification devices are used to authenticate users, then user access can be verified, but bad actors can spoof data and bypass security defenses by presenting falsified information
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing feedback data before authentication decisions are made. Machine learning models pre-process biometric data, device characteristics, and behavioral patterns to establish baseline profiles, enabling the system to detect anomalies and spoofing attempts before they can compromise security.
Solution Approach 2:
The system implements continuous feedback loops where authentication decisions are refined based on ongoing analysis of feedback data. The machine learning models continuously learn from new data points, adjusting their predictions and risk assessments in real-time, allowing the system to adapt to emerging spoofing techniques and improve verification reliability dynamically.
2Reliability
If authentication credentials are requested from users, then security can be verified, but user experience is degraded and authentication processes become more complex
Solution Approach 1:
The system implements self-service authentication by automatically collecting feedback data from multiple sources including biometric sensors, device characteristics, and behavioral patterns without requiring active user participation. The machine learning models autonomously process this data to verify user identity, eliminating the need for users to manually provide authentication credentials while maintaining high security standards.
3Productivity
If all entities attempting to access network resources are processed equally, then no access is denied, but processor and memory resources are wasted on bad actors
Solution Approach 1:
The system applies local quality by treating different entities differently based on their characteristics. Machine learning models analyze feedback data to assign risk scores to each access attempt, allowing the system to apply different processing intensities and security measures to different entities. High-risk attempts receive more intensive analysis while low-risk attempts are processed efficiently, optimizing resource allocation.
Solution Approach 2:
The system performs partial action by selectively applying full security processing only to entities that exhibit suspicious patterns. For legitimate users with established profiles, the system uses streamlined verification processes that consume fewer resources. This approach ensures thorough security checking where needed while minimizing processing overhead for routine access attempts.
Data Source
AI summary
A system comprises a memory communicatively coupled to at least one processor. The at least one processor is configured to receive a communication operation associated with an entity and execute a machine learning algorithm to determine feedback data in the communication operation, determine categorization formats associated with multiple datapoints in the feedback data, assign a specific weighted value to each datapoint based on respective categorization formats, compare the datapoints to the reference datapoints; determine whether the datapoints at least partially matches the reference datapoints, determine multiple weighted values for each of the datapoints that match the reference datapoints, aggregate the weighted values into a match value; determine whether the match value is less than a value threshold, and determine that the entity is associated with the one or more user profiles in response to determining that the match value is less than the value threshold.