Automated Honeypot State Modeling for Safe Target Mimicry
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Solution Overview
Problem
Implementing suitable honeypots for specific needs and target systems requires considerable manual work by experts, making it difficult to deploy and configure effectively.
Innovation Solution
A method for creating a honeypot that involves sending messages to a target system, observing reactions, generating a state machine model, determining vulnerability chains, and removing states to automate the honeypot implementation, including automatic adaptation of operating system behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual configuration methods are used to create honeypots, then the honeypot can be customized for specific target systems, but the deployment process requires considerable manual work by experts and is difficult to deploy effectively
Solution Approach 1:
The system performs self-service by automatically generating state machine models through automated interaction with target systems. The honeypot configuration process is self-configuring, eliminating the need for expert manual intervention while maintaining system-specific accuracy through automated observation and model generation.
Solution Approach 2:
The invention changes parameters by dynamically adapting the state machine model parameters based on observed target system behavior. The honeypot configuration parameters are automatically adjusted through systematic exploration of target system responses, transforming manual configuration into an automated parameter optimization process.
2Reliability
If a complete state machine model is generated for the target system, then the honeypot can accurately mimic the target system behavior, but attackers may exploit the honeypot to attack third-party systems
Solution Approach 1:
The invention extracts only the necessary states for accurate mimicry while removing dangerous states that could enable third-party attacks. By selectively extracting and retaining specific state machine states, the system maintains reliability for detection purposes while eliminating harmful capabilities.
Solution Approach 2:
The system applies preliminary anti-action by proactively removing or neutralizing states that could be exploited for attacks on third-party systems. Before deploying the honeypot, dangerous states are identified and eliminated, preventing potential abuse while preserving the honeypot's ability to detect and analyze attack patterns.
3Adaptability or versatility
If the honeypot provides comprehensive system functionality, then it becomes a more powerful tool for attackers, but this increases the risk that attackers will use it against third parties
Solution Approach 1:
The invention converts the potential harm of a powerful honeypot into a benefit by using the state machine model to identify and remove dangerous capabilities. The comprehensive system analysis that could create security risks is transformed into a filtering mechanism that eliminates threats while preserving analytical value.
Solution Approach 2:
The system segments the honeypot functionality by dividing the state machine model into safe and dangerous states. By segmenting capabilities in this way, the honeypot retains sufficient versatility for threat analysis while isolating and removing the specific functions that could harm third-party systems.
Data Source
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AI summary
According to various embodiments, a method for creating a honeypot is described, comprising sending messages to a target system, observing reactions of the target system to the messages, generating, according to the observed reactions of the target system, a state machine model for one or more interfaces of the target system, determining, for each one or more known vulnerabilities, a chain of states of the state machine model which, when traced, enables exploitation of the vulnerability, removing, for each of the one or more vulnerabilities, at least one state of the chain from the state machine model and generating a honeypot which reacts to messages according to the state machine model.