Token Validation via Sequence Prediction and Reputation
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Solution Overview
Problem
Existing security token systems are vulnerable to fraudulent tokens generated using compromised private keys, leading to unauthorized access to computer resources.
Innovation Solution
The system detects fraudulent security tokens by tracking sequence numbers and predicting future sequence numbers, allowing for validation even when the token issuing authority is unavailable, and utilizing reputation data to determine token validity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the token issuing authority is made unavailable or offline, then fraudulent tokens cannot be validated, but legitimate tokens may also be rejected (false positives)
Solution Approach 1:
The system performs preliminary actions by collecting token issuance data, sequence numbers, and metadata from the token issuing authority before it goes offline. This historical data is stored and used to establish baseline patterns for legitimate token issuance, enabling validation to continue even when the authority is unavailable.
Solution Approach 2:
A forensic server acts as an intermediary between the token issuing authority and the validation system. When the authority is offline, the forensic server uses stored historical data and analysis algorithms to mediate validation decisions, reducing false positives while maintaining fraud detection capability.
2Productivity
If traditional validation methods are used requiring online authority, then fraud detection is simple, but operational efficiency decreases due to false positives
Solution Approach 1:
The validation system is segmented into multiple independent components: a forensic server for offline analysis, local validation modules for quick checks, and data storage systems for historical token information. This segmentation allows the system to operate efficiently in both online and offline modes without requiring complete system complexity.
3Reliability
If sequence number prediction is implemented, then validation can proceed offline, but false positives may increase without proper calibration
Solution Approach 1:
The system implements feedback mechanisms where validation results, both positive and negative, are fed back into the sequence number prediction model. This continuous feedback allows the system to learn from actual token issuance patterns, adjust prediction algorithms, and reduce false positives over time while maintaining offline validation capability.
Data Source
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AI summary
Disclosed embodiments provide for detection of fraudulent electronic security tokens. A compromised private key allows forgery of electronic security tokens, which then allow access to computer resources. Some embodiments track sequence numbers issued by a token issuing authority and are then able to predict sequence numbers issued by the token issuing authority going forward. Some embodiments also determine validity of a token based, at least in part, on a service or client attempting to access resources using the token. For example, some of the disclosed embodiments maintain reputation data for clients or services utilizing electronic tokens, and make determinations on whether a token is likely valid based on the client or services reputation.