Automatic Honeypot Generation via Request-Response Templates
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Solution Overview
Problem
Setting up and configuring low-interaction honeypots to monitor malicious activities and detect unknown attacks is a complex and resource-intensive task, requiring manual effort and computational resources, making it challenging to emulate a large range of systems and applications effectively.
Innovation Solution
The honeypot factory unit automatically generates and deploys low-interaction honeypots by identifying representative applications, probing and parsing request-response pairs to create templates, and using these templates to convincingly emulate the behavior of real systems, reducing manual intervention and computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual generation and configuration of low-interaction honeypots is used, then the honeypot can accurately emulate real systems, but the setup process becomes complex and resource-intensive
Solution Approach 1:
The system performs preliminary actions by automatically probing target applications, capturing request-response pairs, and generating templates before deploying honeypots. This preliminary template generation phase captures the essential behavior patterns of real systems, enabling accurate emulation without manual configuration complexity during deployment.
Solution Approach 2:
The system creates simplified copies of real system behavior through templates that capture essential request-response patterns. Instead of copying entire complex systems, the honeypot uses template-based representations that replicate observable behavior, reducing setup complexity while maintaining emulation accuracy for security monitoring purposes.
2Adaptability or versatility
If multiple low-interaction honeypots are deployed to cover a large application landscape, then comprehensive attack detection is achieved, but computational resources and manual effort increase significantly
Solution Approach 1:
The template generation system creates universal templates that can represent multiple similar applications and services. A single template can emulate various applications with similar request-response patterns, allowing one honeypot instance to provide multi-functional coverage across different application types, thereby increasing deployment efficiency while maintaining broad attack detection capability.
Solution Approach 2:
The system performs preliminary analysis and template generation for multiple applications in advance, capturing their behavior patterns before deployment. This upfront work enables rapid instantiation of multiple honeypots without proportional increases in manual configuration effort, as the templates serve as pre-prepared blueprints for quick deployment across the application landscape.
3Ease of operation
If manual configuration of each honeypot is performed, then precise control over honeypot behavior is achieved, but the time and effort required for deployment increases
Solution Approach 1:
The system enables self-service by automatically probing target applications, capturing their behavior patterns, and generating appropriate honeypot templates without manual intervention. The automated template generation process preserves precise control over honeypot behavior by learning directly from the target application's actual responses, while eliminating the time-consuming manual configuration process.
Solution Approach 2:
The system uses feedback from actual application responses during the template generation phase. By capturing and analyzing real request-response pairs, the generated templates inherit precise behavioral characteristics from the target application, maintaining control accuracy while automating the configuration process to reduce deployment time.
Data Source
AI summary
Various embodiments of systems, computer program products, and methods to automatically generate low-interaction honeypots to protect application landscapes through are described herein. In an aspect, representative applications associated with resources in a network are identified. The low-interaction honeypots are automatically generated for the identified representative applications. Further, the representative applications are probed to retrieve responses corresponding to different requests. Templates are generated corresponding to request-response pairs by parsing the responses and the requests. During operation, new requests for accessing the resources are responded based on the generated templates. The new requests and corresponding responses are recorded.