Automated Honeypot Creation via State Machine Modeling
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Solution Overview
Problem
Implementing suitable honeypots for specific needs and target systems requires significant manual effort from experts, making it inefficient and labor-intensive.
Innovation Solution
A method for creating a honeypot by sending requests to a target system, observing responses, and creating a state machine model for the network protocol behavior, allowing for automatic creation of a honeypot that mimics the target system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual configuration of honeypots is performed by experts, then the honeypot can be customized for specific needs, but the process requires significant manual effort and time
Solution Approach 1:
The system performs self-service by automatically generating honeypot configurations through state machine learning. The honeypot creation process does not require manual expert configuration but instead autonomously observes target system responses and generates appropriate state machine models, eliminating the need for manual intervention while maintaining customization accuracy.
Solution Approach 2:
The invention copies the behavior patterns of target systems by observing their responses to requests and replicating these patterns in the honeypot through state machine models. This copying approach allows the honeypot to mimic realistic system behavior without requiring manual configuration of each interaction scenario, significantly reducing setup time while preserving behavioral accuracy.
2Manufacturing precision
If manual expert work is used to create honeypots, then the honeypot can accurately mimic target systems, but the process becomes labor-intensive and inefficient
Solution Approach 1:
The system uses feedback mechanisms by observing the target system's responses to requests and using this information to refine and generate accurate state machine models. This feedback loop ensures that the honeypot learns the actual behavior patterns of the target system, maintaining high mimicry accuracy while automating the creation process and improving productivity.
Solution Approach 2:
The invention replaces the mechanical process of manual expert configuration with an automated computational system that uses state machine learning. Instead of experts manually analyzing and configuring honeypot behavior, the system automatically observes target system responses and generates appropriate state machine models, maintaining accuracy while dramatically improving creation efficiency.
3Adaptability or versatility
If a honeypot is created to mimic specific network protocol versions, then it can provide targeted security analysis, but manual adaptation to different versions is required
Solution Approach 1:
The system achieves universality by creating a single honeypot framework that can adapt to multiple network protocol versions through state machine learning. Instead of requiring separate manual configurations for each protocol version, the system automatically learns and adapts to different protocols by observing their specific response patterns, enabling one honeypot to serve multiple protocol analysis functions without increasing operational complexity.
Data Source
AI summary
A method for creating a honeypot. The method includes sending requests to a target system; observing the responses of the target system to the requests; in accordance with the observed responses of the target system, creating a state machine model for the behavior of a network protocol according to which the target system responds to requests; and creating a honeypot that responds to requests in accordance with the state machine model.