Data Filtering System Using Specificity Analysis for Threat Detection
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Solution Overview
Problem
Existing systems fail to effectively detect and filter potentially harmful electronic data, such as spam, viruses, and malware, which can compromise network security by unauthorized access and spread dangerous content.
Innovation Solution
A filtering system comprising an interface, extraction generators, specificity generators, and a filter generator that extracts identifiers and attributes from electronic data, calculates specificity, and creates filters based on thresholds to detect and block harmful data by analyzing and normalizing identifiers and attributes, and applying filters to prevent delivery of threatening content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional filtering methods are used to detect harmful electronic data, then the system structure remains simple, but the detection accuracy and reliability are insufficient
Solution Approach 1:
The filtering system is divided into multiple specialized generators: extraction generators for identifying data characteristics, specificity generators for calculating unique identifiers, and filter generators for creating detection rules. Each component handles a specific aspect of the filtering process, improving overall detection accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The system introduces a new dimension of analysis by calculating specificity values based on multiple attributes simultaneously. Instead of relying on single-attribute filtering, the system evaluates combinations of attributes (subject, body, attachments, headers) to generate composite specificity scores, enabling more accurate detection of harmful data patterns.
2Measurement precision
If comprehensive attribute analysis is performed on electronic data, then detection precision improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary extraction of key attributes (subject, body, attachments, headers) and calculates specificity values before the actual filtering decision. By pre-processing and storing these characteristic values, the system avoids redundant analysis during the filtering phase, reducing processing time while maintaining high detection precision.
Solution Approach 2:
The system applies different levels of analysis to different parts of the electronic data. Critical fields like subject lines and attachment names receive more intensive specificity analysis, while less critical portions undergo lighter processing. This selective approach maintains high precision for key indicators while reducing overall computational burden.
3Reliability
If strict filtering criteria are applied to block harmful data, then security reliability improves, but the loss of legitimate data increases
Solution Approach 1:
The system dynamically adjusts filtering parameters based on calculated specificity values. Instead of applying fixed strict criteria to all data, the system modifies filtering thresholds according to the unique specificity profile of each electronic data item. This adaptive approach maintains high security reliability for truly harmful data while preserving legitimate communications that happen to share some superficial characteristics with harmful data.
Data Source
AI summary
A method includes receiving electronic data, extracting a first identifier from the electronic data, extracting first attributes from the electronic data, and searching a database for identifiers that match the first identifier to determine a number of matching identifiers. The method also includes determining that the number of matching identifiers exceeds a first threshold and searching the database for attributes associated with each of the matching identifiers to determine a subset of matching attributes. The method further includes calculating a specificity for the subset of matching attributes, determining that the specificity of the subset of matching attributes is less than or equal to a second threshold, and creating a filter based at least in part on the determination that the specificity of the subset of matching attributes is less than or equal to the second threshold.