Modular Virtual Training Environments for Scalable Cyber Exercises
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Solution Overview
Problem
Current cyber training exercises face challenges in scalability, flexibility, and cost due to the need for expensive and inflexible training environments that are often limited to a single location, requiring significant effort and resources for development and deployment, and are difficult to maintain and update.
Innovation Solution
The development of modular multi-unit training environments that can be deployed on virtual machines, allowing for geographically distributed training and using software agents to collect data and provide interactive dashboards for planning, monitoring, and evaluating cyber exercises, which can be easily moved between different training environments and support various hardware platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional training environments are used, then training exercises can be conducted with sophisticated capabilities, but the cost is high and the flexibility is limited due to single-location deployment
Solution Approach 1:
The training environment is divided into modular virtual machine components that can be independently deployed and configured. Each virtual machine represents a separable unit of the training system, allowing selective deployment across different locations and hardware platforms without requiring complete system replacement.
Solution Approach 2:
Virtual machine images of the training environment are copied and deployed across multiple remote host computing systems. This allows the same sophisticated training capabilities to be replicated across different locations and hardware platforms, providing flexibility without developing separate systems for each location.
2Adaptability or versatility
If training environments are deployed at multiple locations, then geographic flexibility is improved, but development and deployment effort increases significantly
Solution Approach 1:
The training environment is pre-configured into standardized virtual machine images that contain all necessary software, configurations, and training materials. This preliminary preparation allows rapid deployment to multiple locations without requiring time-consuming on-site setup and configuration at each remote host.
Solution Approach 2:
The virtual machine training environment is designed to be universally compatible with multiple hardware platforms and operating systems. This multi-functionality allows the same training exercise to be deployed across diverse remote hosts without developing platform-specific versions, significantly reducing deployment time.
3Reliability
If traditional training systems are used, then comprehensive training content can be provided, but maintenance and updates are difficult and resource-intensive
Solution Approach 1:
The training environment is implemented as dynamic virtual machines that can be easily updated, modified, and replaced. Updates are deployed by replacing virtual machine images rather than modifying physical systems, allowing comprehensive training content to be maintained and updated efficiently across all deployment locations.
Solution Approach 2:
The virtual machine architecture enables automated maintenance and update deployment. The training system can self-update by replacing virtual machine images across the network, reducing the need for manual intervention at each remote host while maintaining comprehensive and current training content.
Data Source
AI summary
An example method includes deploying, by a modular training system and on one or more virtual machines in a network, one or more training environments that are configured to execute one or more training exercises; deploying, by the modular training system and for execution by one or more remote host computing systems that are communicatively coupled to the network, one or more software agents that are executed during the one or more training exercises, wherein the one or more software agents are configured to collect parameter data from the one or more remote host computing systems while the one or more trainees perform actions during the training exercise; and receiving, by the modular training system and from the one or more remote host computing system via the network, the parameter data collected by the one or more software agents during execution of the one or more training exercises.