Firewall Traffic Analysis Using Machine Learning and Formal Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Ongoing monitoring and analysis of network traffic patterns by firewalls to identify anomalous traffic is challenging due to the lack of preexisting knowledge, leading to false conclusions and cascading debug iterations.
Innovation Solution
A combined approach using machine learning and formal techniques to generate n-dimensional vector representations of traffic logs and a formal model of firewall rules, enabling cluster analysis and evaluation of both observed and hypothetical network traffic to enhance firewall debugging and identify correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained solely on observed network traffic logs, then the model can identify patterns in actual traffic, but it cannot reliably determine hypothetical traffic scenarios or provide complete firewall debugging insights
Solution Approach 1:
The patent combines machine learning models with formal verification techniques to create a hybrid system. The ML component analyzes observed traffic patterns from logs, while the formal verification component validates hypotheses about hypothetical traffic scenarios. This merging allows the system to leverage the strengths of both approaches: data-driven pattern recognition and rigorous logical reasoning, thereby achieving both accurate detection and comprehensive analysis capabilities.
Solution Approach 2:
The patent introduces a formal verification module as an intermediary between the ML model and the final analysis results. This intermediary component takes the ML model's findings and formally verifies them against the firewall configuration and hypothetical scenarios. The formal verification acts as a mediator that ensures the ML-based conclusions are logically sound and complete, bridging the gap between empirical observation and theoretical possibility.
2Reliability
If formal verification is used to analyze all possible network traffic scenarios, then complete firewall rule validation is achieved, but computational complexity and analysis time increase significantly
Solution Approach 1:
The patent segments the network traffic analysis task into two distinct phases: a machine learning-based preliminary analysis phase and a formal verification-based validation phase. The ML phase quickly identifies potential anomalies and generates hypotheses about interesting traffic patterns. The formal verification phase then focuses only on validating these specific hypotheses rather than analyzing all possible scenarios. This segmentation dramatically reduces the computational burden while maintaining comprehensive validation coverage.
Solution Approach 2:
The patent performs preliminary action by using machine learning models to pre-process and filter network traffic data before formal verification. The ML model identifies the most relevant traffic patterns and generates targeted hypotheses for verification. This preliminary filtering action eliminates the need for formal verification to exhaustively check all possible traffic scenarios, thereby reducing analysis time while maintaining reliability through focused verification of the most promising candidates.
3Loss of information
If comprehensive cluster analysis is performed on all traffic log entries, then detailed network behavior patterns are discovered, but processing resources and computational overhead increase
Solution Approach 1:
The patent applies partial action by performing cluster analysis only on a strategically selected subset of traffic log entries rather than all entries. The machine learning model first identifies and prioritizes the most informative and anomalous traffic patterns for detailed analysis. By focusing cluster analysis on this prioritized subset, the system achieves valuable network behavior insights without the excessive computational overhead of analyzing every single log entry in detail.
Data Source
AI summary
A system generates vector representations of entries of traffic logs generated by a firewall. A first model learns contexts of values recorded in the logs during training, and the system extracts vector representations of the values from the trained model. For each log entry, vectors created for the corresponding values are combined to create a vector representing the entry. Cluster analysis of the vector representations can be performed to determine clusters of similar traffic and outliers indicative of potentially anomalous traffic. The system also generates a formal model representing firewall behavior which comprises formulas generated from the firewall rules. Proposed traffic scenarios not recorded in the logs can be evaluated based on the formulas to determine actions which the firewall would take in the scenarios. The combination of models which implement machine learning and formal techniques facilitates evaluation of both observed and hypothetical network traffic based on the firewall rules.