Large Language Model Honeypot Configuration for Credible Attack Simulation
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Solution Overview
Problem
Existing honeypot configurations struggle to effectively simulate target systems in a credible and complete manner, failing to attract and maintain attacker interest for thorough interaction analysis.
Innovation Solution
Utilizing a large language model to conduct attacks on honeypots, assess their quality, and adapt configurations based on interaction metrics to enhance credibility and attractiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If honeypot configurations are simplified for ease of deployment, then ease of manufacture is improved, but credibility and completeness in simulating target systems deteriorates
Solution Approach 1:
The honeypot system dynamically adapts its configuration based on attacker behavior and assessment results. The configuration is not static but evolves through automated testing with large language models, allowing the system to maintain simplicity in deployment while achieving high credibility through continuous optimization of simulation accuracy.
Solution Approach 2:
The system changes configuration parameters automatically based on assessment metrics. By using large language models to evaluate attacker interactions and adjust configuration parameters accordingly, the system achieves high credibility without requiring manual configuration complexity, thus resolving the contradiction between ease of deployment and simulation accuracy.
2Reliability
If honeypot configurations are manually optimized to improve credibility, then reliability is improved, but productivity deteriorates due to time-consuming manual processes
Solution Approach 1:
The honeypot configuration system performs self-optimization through automated assessment and adjustment using large language models. Instead of requiring manual intervention for configuration optimization, the system autonomously evaluates attacker interactions and adjusts its own configuration, thereby maintaining high credibility while eliminating time-consuming manual processes and improving productivity.
Solution Approach 2:
The system implements a feedback loop where large language models assess attacker interactions with the honeypot and provide information used to automatically adjust configurations. This closed-loop feedback mechanism enables continuous optimization of credibility without manual intervention, resolving the contradiction between reliability improvement and productivity maintenance.
3Reliability
If honeypot configurations are highly specific to particular target systems to improve credibility, then reliability is improved, but adaptability deteriorates
Solution Approach 1:
The honeypot system achieves universal applicability across different target systems through a standardized configuration framework that uses large language models for assessment. The same automated optimization process can be applied to various target systems, allowing the honeypot to adapt to different contexts while maintaining high credibility through systematic configuration adjustment rather than system-specific customization.
Solution Approach 2:
The system dynamically adapts its configuration based on the specific target system being simulated, using large language models to assess and optimize for each context. This dynamic adaptation capability allows the honeypot to maintain high credibility for particular targets while retaining overall versatility through the same automated process, resolving the contradiction between specificity and adaptability.
4Productivity
If automated assessment using large language models is implemented to improve productivity, then productivity is improved, but device complexity deteriorates
Solution Approach 1:
Large language models serve as an intermediary between the honeypot configuration system and the assessment process. By introducing this AI-based mediator, the system achieves automated high-speed assessment without requiring complex custom-built evaluation tools, thus improving productivity while managing complexity through the use of a sophisticated but modular intermediary component.
Data Source
AI summary
A method for configuring a honeypot. The method includes: implementing a honeypot, conducting at least one attack on the honeypot by means of a large language model, ascertaining an assessment of the at least one attack; and configuring the honeypot depending on the assessment of the at least one attack.