Fuzzing Data Aggregation for Network Vulnerability Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing network filtering technologies face challenges in efficiently detecting and preventing vulnerabilities in connected devices, particularly due to the large volume of test data generated by fuzzing tests, which leads to storage issues and prolonged filtering times.
Innovation Solution
A filtering system that includes a vulnerability detection unit and a filtering unit, where the detection unit generates and aggregates test patterns to identify fault-causing data, and the filtering unit compares input data against aggregated prevention target lists to quickly filter out potentially harmful inputs, reducing data storage needs and filtering time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sets of test data are generated for fuzzing tests, then vulnerability detection capability is improved, but storage area becomes tight and filtering time increases
Solution Approach 1:
The patent merges multiple fault-causing data sets by identifying common characteristics and patterns among them. The aggregation unit combines multiple sets of fault-causing data into a single aggregated data set, reducing storage requirements while preserving vulnerability detection capability. This is achieved by extracting common features from multiple test data sets that caused faults, thereby consolidating redundant information.
Solution Approach 2:
Instead of storing all raw test data sets, the system creates an aggregated representation that copies only the essential common characteristics. The aggregation unit generates a condensed version of multiple fault-causing data sets, storing the aggregated result rather than the original multiple sets, thus reducing storage space while maintaining the ability to detect similar vulnerabilities.
2Reliability
If multiple sets of test data are generated for fuzzing tests, then vulnerability detection capability is improved, but filtering time increases
Solution Approach 1:
The patent merges multiple fault-causing data sets by identifying common characteristics and patterns among them. The aggregation unit combines multiple sets of fault-causing data into a single aggregated data set, reducing storage requirements while preserving vulnerability detection capability. This is achieved by extracting common features from multiple test data sets that caused faults, thereby consolidating redundant information.
Solution Approach 2:
The system performs aggregation of fault-causing data sets in advance, before the actual filtering operation. By pre-processing and consolidating multiple test data sets into an aggregated form, the system reduces the computational burden during runtime filtering, thereby decreasing filtering time while maintaining comprehensive vulnerability detection.
3Loss of time
If aggregated data is used for filtering, then filtering time is reduced, but data aggregation processing is required
Solution Approach 1:
The patent segments the filtering system into distinct functional units: a fault detection unit that identifies faults in test data, and an aggregation unit that processes the fault information. This segmentation allows the aggregation processing to be handled by a dedicated component, making the overall system more manageable despite the added complexity of aggregation operations.
Solution Approach 2:
The aggregation unit acts as an intermediary between the fault detection process and the filtering process. It receives multiple fault-causing data sets, processes them to extract common characteristics, and outputs an aggregated data set that can be efficiently used for filtering. This intermediary component simplifies the interface between detection and filtering operations.
Data Source
AI summary
An information processing device includes a processor configured to generate one or more sets of data corresponding to information about a testing method set in advance, to input the sets of generated data into a test device, to identify sets of data making the test device exhibit predetermined behavior, among the sets of generated data, and to refer to information common among the sets of identified data, to aggregate the sets of generated data.