Automated Penetration Testing via Traffic Analysis
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Solution Overview
Problem
Existing penetration testing methods for IT environments are manual, time-consuming, and often ineffective in identifying vulnerabilities across complex systems that span multiple layers from hardware to application, due to the complexity of information traffic and communication protocols, making it difficult to develop effective test plans that meet compliance requirements.
Innovation Solution
An automated system for generating penetration tests using a traffic capture engine, filtering engine, traffic analysis engine, and recommendation engine that captures and analyzes information traffic, identifies vulnerabilities, and produces targeted penetration test plans based on system profiles and a dynamic knowledge base, enabling efficient assessment from the hardware layer to the application layer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual penetration testing methods are used, then flexibility and customization are maintained, but the process becomes time-consuming and less effective in identifying vulnerabilities across complex multi-layer systems
Solution Approach 1:
The patent replaces manual mechanical analysis methods with automated machine-learning-based systems. The ML model automatically analyzes information traffic, identifies communication protocols, and generates penetration test plans without manual intervention, thereby increasing productivity while reducing time consumption.
Solution Approach 2:
The patent introduces an automated system with ML models as an intermediary between the complex IT environment and the penetration testing process. This intermediary automatically captures information traffic, analyzes protocols, and generates test plans, resolving the contradiction by automating the previously manual process.
2Measurement precision
If comprehensive penetration testing across multiple layers is performed, then vulnerability identification improves, but the complexity of analyzing information traffic and communication protocols increases
Solution Approach 1:
The patent uses machine learning models to replace complex manual analysis mechanisms. The ML model automatically identifies communication protocols and analyzes information traffic across multiple layers, maintaining high vulnerability identification accuracy while reducing the perceived complexity through automation.
Solution Approach 2:
The patent creates simplified representations (copies) of complex communication protocols and information traffic patterns through automated analysis. The ML model learns from captured traffic and generates test plans based on these simplified models, making the complex analysis manageable while maintaining accuracy.
3Productivity
If automated systems are introduced to improve testing efficiency, then productivity increases, but the complexity of the testing system itself increases
Solution Approach 1:
The patent creates a universal automated testing system that can handle multiple communication protocols and IT infrastructure layers through a single ML model. This multi-functional system improves productivity while managing complexity by consolidating multiple testing capabilities into one unified platform.
Solution Approach 2:
The patent uses parameter changes in the ML model to adapt to different testing scenarios and protocols. By dynamically adjusting model parameters rather than creating separate systems for each scenario, the patent achieves high productivity while controlling system complexity through flexible parameter management.
Data Source
AI summary
In some examples, a system receives information traffic communicated over a network by or with a system under test (SUT), and analyzes the information traffic to identify a potential attack point in the SUT and a technology used by the SUT. The system determines a collection of penetration tests for testing a stack comprising a plurality of layers associated with the SUT based on the identified potential attack point and the identified technology, and further based on a dynamic knowledge base that includes information relating to vulnerabilities and threats.