Network Ruleset Misconfiguration Detection Using Sample Data Flows
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Solution Overview
Problem
The misconfiguration of network policies in network components leads to incorrect redirection or termination of data packets, causing issues such as blocking essential services and potential violations of end-user licensing agreements.
Innovation Solution
A method and system for detecting ruleset misconfiguration by generating sample data flows, evaluating them against a predetermined definition to generate flow descriptors, and assessing how they perform with respect to a ruleset, with the option to determine a response if a threshold of adverse impact is exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network policies are applied to block or redirect data packets, then network security and data management are improved, but misconfiguration may occur causing incorrect redirection or termination of data packets
Solution Approach 1:
The system performs preliminary evaluation of sample data flows against the ruleset before full deployment. By pre-testing the ruleset configuration with sample flows and comparing results against expected outcomes, the system can detect misconfigurations before they affect actual network traffic, thus preventing incorrect redirection while maintaining security policies.
Solution Approach 2:
The system implements feedback mechanisms by evaluating sample data flows against the ruleset and comparing actual results with expected results. This feedback loop enables detection of misconfigurations where data flows are incorrectly blocked or redirected, allowing the system to identify and report issues without disrupting normal network operations.
2Measurement precision
If ruleset evaluation is performed on all data flows, then detection accuracy is improved, but processing time and system complexity increase
Solution Approach 1:
The system segments the evaluation process by using sample data flows instead of processing all actual data flows. This segmentation allows the system to maintain detection accuracy by evaluating representative samples against the ruleset while significantly reducing processing time and computational complexity compared to full population analysis.
Solution Approach 2:
The system creates copies of actual data flows in the form of sample data flows that replicate the characteristics and patterns of real traffic. By evaluating these copies against the ruleset, the system achieves detection accuracy comparable to analyzing all actual flows while using a fraction of the processing resources and time.
3Productivity
If sample data flows are used for ruleset evaluation, then processing efficiency is improved, but the ability to detect all potential misconfigurations may be reduced
Solution Approach 1:
The system applies local quality by carefully selecting and constructing sample data flows that represent specific local characteristics of network traffic patterns. Each sample flow is designed to test particular aspects of the ruleset, ensuring that detection reliability is maintained for the most critical misconfiguration scenarios while preserving processing efficiency through targeted sampling rather than exhaustive analysis.
Data Source
AI summary
A method and system for detecting ruleset misconfiguration in a computer network. The method including: generating a set of sample data flows; evaluating each of the set of sample data flows against a predetermined definition to generate a flow descriptor for each of the set of sample data flows; evaluating each of the flow descriptors against a ruleset; and generating a summary of how each of the flow descriptors perform with respect to the ruleset. The system including: a data flow sample retrieval module configured to generate flows; a policy engine configured to store and retrieve a ruleset; a packet processing engine configured to evaluate each of the data flows against a predetermined definition to generate a flow descriptor for each flows, evaluate each of the flow descriptors against a ruleset; and generate a summary of how each of the flow descriptors perform with respect to the ruleset.