Password Fraud Detection via Frequency Analysis
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Solution Overview
Problem
Current network technologies lack effective mechanisms to detect and prevent password fraud, often relying on inadequate security measures that can be breached by malicious actors, leaving consumers' personal and financial information vulnerable without sufficient user input or notification.
Innovation Solution
Implementing a password fraud detection feature that compares incoming password requests against a frequency of use database to flag potentially compromised accounts, requiring additional verification such as CAPTCHA or security questions to prevent unauthorized access, and automatically updating passwords to enhance security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security measures are used to protect consumer information, then implementation is simple, but security reliability is insufficient and can be breached by malicious actors
Solution Approach 1:
The system performs preliminary actions by maintaining a database of compromised passwords and detecting password requests before they can be used for fraudulent access. The fraud detection system proactively identifies potentially compromised accounts by comparing incoming password requests against the database of known compromised passwords, preventing unauthorized access before it occurs.
Solution Approach 2:
The patent introduces an intermediary fraud detection system that sits between the user and the account access system. This intermediary layer analyzes password requests, checks them against the compromised password database, and determines whether to allow or block access based on the analysis, adding a security layer without completely redesigning the existing authentication infrastructure.
2Measurement precision
If frequency of use analysis is implemented to detect compromised passwords, then detection capability is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by selectively analyzing only those password requests that match entries in the compromised password database, rather than performing exhaustive analysis on all authentication attempts. This targeted approach maintains high detection accuracy for compromised passwords while minimizing unnecessary processing time for legitimate authentication requests.
Solution Approach 2:
The patent replaces complex mechanical analysis with a more efficient information-based approach. Instead of analyzing behavioral patterns, device characteristics, or other complex factors, the system substitutes a direct database matching mechanism that compares incoming passwords against known compromised passwords, significantly reducing processing time while maintaining detection accuracy.
3Reliability
If additional verification steps are required for potentially compromised accounts, then security is enhanced, but user convenience deteriorates
Solution Approach 1:
The system applies local quality by implementing enhanced verification measures only for specific accounts that are identified as potentially compromised, rather than applying uniform additional verification to all users. This targeted approach maintains high security for at-risk accounts while preserving user convenience for the majority of legitimate users whose accounts show no signs of compromise.
Solution Approach 2:
The fraud detection system performs preliminary analysis of password requests against the compromised password database before user access is granted. By detecting potentially compromised accounts in advance and flagging them for additional verification, the system prepares the necessary security measures beforehand, reducing the impact on user convenience during the actual authentication process.
Data Source
AI summary
Techniques for marking or flagging an account as potentially being compromised may be provided. Information about the popularity of passwords associated with a plurality of accounts may be maintained. In an example, an account may be marked as potentially being compromised based at least in part on the information about the popularity of passwords and a password included in a request to change the password associated with the account. A notification indicating that an account has been marked as potentially compromised may be generated.