Synthetic Threat Data Injection for Protective Model Validation
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Solution Overview
Problem
Existing methods for testing malware detection models in corporate networks are difficult to scale and require significant manual effort, making it challenging to continuously validate the effectiveness and resilience of these models against changing attacker behaviors.
Innovation Solution
Automatically generate synthetic threat data, such as command and control data, by infecting virtual machines with pseudo-malicious agents, collect simulated network traffic, and inject this data into genuine traffic to create a composite stream for monitoring by protective models, flagging failures as vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual testing methods are used to validate protective models, then testing can be performed with simple tools, but the testing process requires significant manual effort and is difficult to scale
Solution Approach 1:
The patent creates synthetic copies of malicious network traffic through virtual machines infected with pseudo-malicious agents. These synthetic threat data copies simulate real attacker behavior without requiring actual malware, enabling automated testing while reducing the need for complex manual test setup and execution
Solution Approach 2:
The testing system performs self-service by automatically generating synthetic threat data, injecting it into genuine network traffic, and evaluating protective model responses without continuous manual intervention. The system autonomously validates protective models against simulated attacks, reducing manual effort while maintaining test complexity through automated infrastructure
2Reliability
If real attacker-operated malicious activity is used for testing, then the effectiveness of protective models can be validated, but this requires actual malware deployment which is highly undesirable
Solution Approach 1:
The patent converts the harmful nature of real malware into a beneficial testing mechanism by creating pseudo-malicious agents that simulate attacker behavior. These synthetic threats provide reliable validation of protective model effectiveness without the actual harm of deploying real malware, transforming a potentially dangerous testing approach into a safe yet effective solution
Solution Approach 2:
The patent introduces virtual machines with pseudo-malicious agents as an intermediary between the testing system and real network traffic. This intermediary layer provides realistic threat simulation for validating protective models while eliminating the need to deploy actual malware, thus maintaining detection effectiveness without the associated risks
3Productivity
If behavioral detection models are deployed to monitor network traffic, then automated detection of malware can be achieved, but the models require continuous validation which is resource-intensive
Solution Approach 1:
The patent enables continuous validation of protective models by integrating synthetic threat data generation into the ongoing network traffic stream. Instead of periodic manual testing, the system continuously injects synthetic threats and evaluates model responses in real-time, maintaining constant validation capability without significant time loss while improving overall productivity through automation
Data Source
AI summary
A method for detecting computer vulnerabilities comprises automatically generating synthetic threat data representative of malicious activity, injecting the synthetic threat data into genuine data to create a composite data stream, observing a protective model monitoring the composite data stream, and responsive to determining a failure by the protective model to detect the synthetic threat data, flagging the failure as a vulnerability. The synthetic threat data may be generated by automatically generating a plurality of pseudo-malicious agents, infecting virtual machines connected to a simulated network with the pseudo-malicious agents, and collecting simulated network traffic from the infected virtual machines, where the simulated network traffic contains communications from the pseudo-malicious agents.