Honeypot Deployment Based on Host Lifecycle Metadata
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Solution Overview
Problem
The effective deployment and management of honeypot fleets in complex cybersecurity environments is hindered by arbitrary provisioning, high costs, resource inefficiency, and the need for specialized technical skills, which fails to account for the qualitative and quantitative nature of network environments and their devices.
Innovation Solution
Dynamic deployment and management of honeypots based on a network environment's lifecycle, using lifecycle metadata to determine the appropriate number and configuration of honeypots, enabling proactive and efficient fleet management through a honeypot management server and orchestration engine that deploys or disables honeypots based on a configurable ratio and host replacement operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If honeypots are arbitrarily provisioned in the network, then the deployment is simple, but it bears no relationship to the quantitative and qualitative nature of protected computing systems and results in resource inefficiency
Solution Approach 1:
The system dynamically provisions and discharges honeypots based on real-time lifecycle metadata of protected hosts. The honeypot fleet composition changes adaptively as hosts are added or removed from the network, ensuring honeypot deployment matches the actual quantitative nature of protected systems without arbitrary static provisioning
Solution Approach 2:
The system continuously monitors lifecycle metadata from protected hosts and uses this feedback to adjust honeypot provisioning. The honeypot management receives information about host additions/removals and automatically adjusts the honeypot fleet accordingly, creating a closed-loop system that prevents resource waste while maintaining appropriate deployment levels
2Reliability
If a large fleet of honeypots is deployed to cover complex network environments, then security coverage is improved, but the computational complexity and cost increase significantly
Solution Approach 1:
The honeypot fleet performs self-management through automated provisioning and discharge operations based on lifecycle metadata. The system automatically detects when protected hosts are added or removed and adjusts the honeypot fleet without manual intervention, reducing operational complexity while maintaining appropriate security coverage
Solution Approach 2:
The system proactively provisions honeypots when protected hosts are added to the network and discharges them when hosts are removed. This preliminary and reactive action based on lifecycle events ensures security coverage is maintained without requiring complex manual fleet management
3Productivity
If honeypots are deployed based on lifecycle metadata and configurable ratios, then resource utilization is optimized, but the system complexity increases due to metadata management requirements
Solution Approach 1:
The system uses a universal configurable ratio parameter that applies across different network segments and host types. This single configurable parameter controls honeypot provisioning behavior universally, simplifying metadata processing while maintaining optimized resource utilization across diverse environments
Data Source
AI summary
Disclosed herein are methods, systems, and processes for dynamically deploying deception computing systems based on network environment lifecycle. Lifecycle metadata associated with protected hosts in a network is retrieved. A configurable ratio of honeypots to the protected hosts is accessed. One or more honeypots are deployed based on: the configurable ratio if the lifecycle metadata can be retrieved or determined, or on a schedule if the lifecycle metadata cannot be retrieved or determined, but can be estimated.