Firewall Utilization Model for Unused Rule Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The complexity of virtual private cloud (VPC) networks and the dynamic nature of firewall rules make it challenging for users to understand and maintain optimal firewall configurations, leading to difficulties in identifying unused or unnecessary rules.
Innovation Solution
A computer-implemented method and system for training a firewall utilization model by receiving firewall utilization data, generating training data based on hit counts for each sub-rule, and determining sub-rule utilization probabilities to predict future rule usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If firewall rules are made comprehensive to cover all possible connection scenarios, then security coverage is improved, but rule complexity increases making it difficult to understand and maintain
Solution Approach 1:
The patent segments the complex firewall rule set into individual sub-rules that can be independently analyzed. Each sub-rule represents a specific condition or action within the firewall configuration, allowing the system to break down the overall complexity into manageable units for assessment and optimization.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between the complex firewall rules and the user. This model automatically analyzes rule utilization patterns, assesses security coverage, and provides recommendations, eliminating the need for users to directly comprehend and manage the complexity of individual rules.
2Ease of operation
If firewall rules are maintained manually over time, then configuration control is preserved, but difficulty in identifying unused rules increases leading to maintenance challenges
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model continuously monitors firewall rule utilization and provides information about which rules are unused or underutilized. This feedback loop enables automatic identification of redundant rules based on actual connection patterns, allowing administrators to maintain optimal firewall configurations without manual tracking.
Solution Approach 2:
The system performs self-service by automatically analyzing firewall log data and identifying unused rules without requiring manual intervention. The machine learning model independently assesses rule utilization patterns and generates maintenance recommendations, reducing the operational burden on administrators.
3Measurement precision
If comprehensive firewall monitoring is implemented to track all connection requests, then rule utilization accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent replaces manual or rule-based analysis mechanisms with a machine learning model that automatically processes firewall monitoring data. This substitution enables precise measurement of rule utilization patterns while the model handles the complexity of data processing, extracting meaningful insights from large volumes of connection request information.
Data Source
AI summary
A computer-implemented method causes data processing hardware to perform operations for training a firewall utilization model. The operations include receiving firewall utilization data for firewall connection requests during a utilization period. The firewall utilization data includes hit counts for each sub-rule associated with at least one firewall rule. The operations also include generating training data based on the firewall utilization data. The training data includes unused sub-rules corresponding to sub-rules having no hits during the utilization period and hit sub-rules corresponding to sub-rules having more than zero hits during the utilization period. The operations also include training a firewall utilization model on the training data. The operations further include, for each sub-rule associated with the at least one firewall rule, determining a corresponding sub-rule utilization probability indicating a likelihood the sub-rule will be used for a future connection request.