Credential Dump Authenticity Verification via Hash Comparison
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Solution Overview
Problem
Current methods are inefficient in verifying the authenticity of credential dumps, leading to unnecessary resource expenditure and potential downtime in investigating suspected data breaches, as they often require manual verification of large datasets to determine if they represent new or previously known breaches.
Innovation Solution
A system that includes a processor and memory for analyzing potential credential dumps by identifying common formats and delimiters, hashing pairs of usernames and passwords, and comparing these hashes to stored records to determine authenticity, with a threshold-based verification process to quickly identify authentic credential dumps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification of credential dumps is performed, then verification accuracy is improved, but time consumption and resource expenditure increase significantly
Solution Approach 1:
The patent replaces manual verification processes with automated computational systems. Specifically, it uses hash-based comparison algorithms and machine learning models to automatically verify credential dump authenticity, eliminating the need for manual inspection while maintaining high accuracy. The system processes credential data through automated pipelines that perform format validation, hash computation, and comparison against known breach databases.
Solution Approach 2:
The patent creates standardized representations (hashes) of credential data that can be efficiently compared without handling the actual sensitive credentials. By copying and hashing credential patterns rather than examining raw data, the system achieves rapid verification while preserving accuracy. This allows multiple verification copies to be generated and compared simultaneously.
2Measurement precision
If comprehensive analysis of credential dumps is performed, then detection precision is improved, but processing speed decreases
Solution Approach 1:
The patent divides the credential verification process into distinct segments: format validation, hash computation, database comparison, and authenticity determination. Each segment processes specific aspects of the credential data independently, allowing parallel execution and optimizing both precision and speed. The segmentation enables the system to apply different verification strategies to different portions of the credential dump.
Solution Approach 2:
The patent performs preliminary formatting standardization and hash computation on credential data before the actual verification comparison. By pre-processing the data into standardized formats and pre-computing hash values, the system eliminates time-consuming operations during the critical comparison phase, thereby improving processing speed without compromising detection precision.
3Productivity
If automated verification systems are implemented, then processing speed is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal verification framework that handles multiple credential formats, delimiters, and data structures through a single automated system. The system is designed to accommodate various credential dump formats (CSV, TXT, JSON) and different delimiter types (commas, semicolons, pipes) without requiring separate verification processes, thereby managing complexity while maintaining high processing speed across diverse input types.
4Productivity
If threshold-based verification is used, then processing efficiency is improved, but verification precision may be reduced
Solution Approach 1:
The patent implements dynamic threshold adjustment mechanisms that adapt verification thresholds based on the specific characteristics of the credential dump being analyzed. The system can adjust thresholds according to data quality, format consistency, and confidence scores from preliminary analysis, thereby maintaining high verification precision while preserving processing efficiency. This dynamic approach allows the system to be more stringent when needed and more lenient when appropriate.
Data Source
AI summary
In some embodiments, an apparatus includes a memory, storing processor-executable instructions, blacklist terms, and credential dump records, and a processor. The processor receives repository data from targeted remote repositories and stores the repository data as a potential credential dump in the memory when the repository data includes a credential dump attribute. The processor stores the potential credential dump as a probable credential dump when the potential credential dump does not include a blacklist term, in which case the processor also detects a format and delimiter of the probable credential dump. Based on the format and delimiter, pairs of usernames and associated passwords are identified and hashed. If a percentage of the hashes not associated with the credential dump records exceeds a predetermined threshold, the probable credential dump is deemed authentic.