Generative AI Honeynet Environments With Consistent Network Artifacts
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Solution Overview
Problem
Conventional honeypots and honeynets lack the ability to create authentic network-level environments to capture sophisticated cybersecurity threats, requiring manual generation of realistic content for workstations and network elements, which is time-consuming and resource-intensive, and can be easily spotted by threat actors.
Innovation Solution
An automated system using a generative AI model generates consistent honeynet environments by querying a large language model with minimal descriptive information, creating network configurations and content that respects previously generated artifacts, reducing time and resources needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual generation of realistic content for workstations and network elements is used, then authenticity of honeynet environment is improved, but time consumption and resource requirements increase
Solution Approach 1:
The system enables automated self-generation of realistic content including employee profiles, network configurations, and documentation. The generative AI model autonomously creates consistent artifacts across the honeynet environment without requiring manual intervention, thereby maintaining high authenticity while dramatically reducing time consumption and resource requirements.
2Reliability
If manual generation of realistic content for workstations and network elements is used, then authenticity of honeynet environment is improved, but resource requirements increase
Solution Approach 1:
The patent replaces the mechanical manual process of creating realistic content with an automated generative AI system. This substitution eliminates the need for human experts to manually craft employee profiles, network configurations, and other artifacts, thereby maintaining environmental authenticity while significantly reducing the human resources and energy required for generation.
3Reliability
If consistent information across honeynet environment is ensured, then deception effectiveness is improved, but system complexity increases
Solution Approach 1:
The generative AI model serves multiple functions simultaneously: it creates employee profiles, generates network configurations, produces documentation, and ensures cross-artifact consistency. This multi-functionality allows the system to maintain high deception effectiveness through consistent information across all honeynet elements without requiring separate complex systems for each artifact type.
Data Source
AI summary
Systems and methods for smart generation of content for a deceptive honeynet environment. The systems and methods generate a first prompt to an artificial intelligence (AI) model to generate a first output based on an initial input, receive the first output from the AI model, the first output comprising a first set of content, generate a second prompt to the AI model to generate a second output comprising a network configuration based on the first set of content and the initial input, receive the second output from the AI model, the second output comprising the network configuration, wherein the network configuration is consistent with the first set of content and the initial input, and store the first set of content and the network configuration.