Database Transaction Token Marker Sequence Analysis
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Solution Overview
Problem
Existing database protection systems face performance issues and potential Denial-of-Service attacks due to the computational complexity of parsing large and complex transactions, which can slow down real-time security threat detection and prevention.
Innovation Solution
The method generates a token marker sequence for database transactions, sorting and minimizing it based on probability to uniquely identify transactions, allowing for efficient parsing and avoiding redundant processing, thereby reducing computational overhead and preventing DoS attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the database protection system parses and analyzes large database transactions in real-time, then security threat detection capability is improved, but system performance deteriorates and computational overhead increases
Solution Approach 1:
The patent segments the database transaction parsing process into distinct phases: generating token markers from transaction statements, sorting markers by probability, and selectively processing based on thresholds. This segmentation allows the system to handle security analysis differently based on transaction characteristics, improving overall performance while maintaining detection capability.
Solution Approach 2:
The patent changes the parameter of transaction processing by introducing probability-based token marker sorting and threshold filtering. Instead of uniformly parsing all transactions, the system dynamically adjusts processing depth based on calculated probabilities, reducing computational overhead for low-risk transactions while maintaining thorough analysis for suspicious patterns.
2Reliability
If the system processes and parses complex database transactions thoroughly, then security monitoring accuracy is improved, but computational complexity increases causing potential Denial-of-Service attacks
Solution Approach 1:
The patent applies preliminary action by generating token markers and calculating their probabilities before full transaction processing. This pre-processing step identifies and filters out common, low-risk transaction patterns early, preventing the system from wasting computational resources on routine operations while reserving full processing power for genuinely suspicious transactions.
Solution Approach 2:
The patent extracts and separates the token marker generation and probability calculation steps from the main transaction processing pipeline. By taking out these preliminary steps, the system can identify and eliminate redundant processing for common transactions, reducing overall computational complexity while maintaining security monitoring accuracy for novel attack patterns.
3Reliability
If the database protection system monitors and analyzes all database transactions, then real-time threat detection is improved, but processing time increases
Solution Approach 1:
The patent changes the processing parameter based on token marker probability thresholds. High-probability (common) transactions receive minimal processing, while low-probability (suspicious) transactions receive full real-time analysis. This parameter-based differentiation maintains real-time threat detection capability for attacks while significantly reducing average processing time across all transactions.
Data Source
AI summary
Embodiments are disclosed for a method for identifying large database transactions. The method includes generating a token marker sequence of a database transaction. The token marker sequence includes multiple token markers. The token markers include a token of the database transaction and a position corresponding to the token. The method further includes sorting the token markers based on a probability that the token occurs in a stream of database transactions. Additionally, the method includes reducing a size of the token marker sequence based on a predetermined threshold.