Dynamic Cyber Training System with AI Opponents
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Solution Overview
Problem
Current cyber security training methods are inadequate in addressing the rapidly evolving cyber threat landscape, as they are often static, costly, and unable to provide the dynamic and realistic training environments needed to effectively train personnel in responding to cyber-attacks.
Innovation Solution
A dynamic, virtual network training system that provides a closed, controlled network environment with varying scenarios and resources, allowing for mission-based, game-like training that includes AI opponents and realistic virtual environments, enabling flexible and scalable training across different industries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current training methods use static, customized systems for each industry, then training can be tailored to specific needs, but the cost increases and the systems become obsolete quickly
Solution Approach 1:
The training system is designed to serve multiple industries and training scenarios through a unified platform. It provides industry-agnostic core functionality that can be configured for different sectors (healthcare, cybersecurity, power grid, etc.) without requiring separate customized systems for each industry, thereby reducing overall complexity while maintaining adaptability.
Solution Approach 2:
The system transitions from static, manually customized training environments to dynamic, automatically generated ones. Training scenarios are programmatically created and updated based on current threats and requirements, allowing the system to adapt quickly without manual reconfiguration and reducing obsolescence.
2Adaptability or versatility
If manual customization is performed for each industry target, then training needs are met, but development and support costs increase
Solution Approach 1:
The system pre-configures core training functionalities and industry templates in advance. When a new training program is needed, the system automatically generates the training environment based on pre-established patterns and current threat data, eliminating the need for expensive manual customization for each new industry or training scenario.
Solution Approach 2:
The training system automatically configures and updates training environments without requiring manual intervention from trainers or system administrators. It self-generates training scenarios, provisions virtual machines, and updates content based on programmed parameters and current threat intelligence, significantly reducing development and support costs.
3Stability of the object's composition
If fixed training applications are used, then training sessions are standardized, but flexibility to adapt to new threats is limited
Solution Approach 1:
The system generates training scenarios dynamically based on current threat data and requirements. Rather than relying on fixed, pre-authored training applications, the system programmatically creates realistic training environments that reflect current threats, maintaining consistency through automated processes while adapting to new threats automatically.
4Reliability
If training systems are updated manually to keep pace with technology changes, then training relevance is maintained, but the pace of updates is too slow
Solution Approach 1:
The training system automatically updates training content and scenarios by pulling data from threat intelligence sources and programmatically generating new training materials. This self-updating capability ensures training remains relevant to current threats without requiring manual updates, dramatically increasing the speed at which training can keep pace with technology changes.
Solution Approach 2:
The system continuously monitors current threats and security landscape changes, using this feedback to automatically update and generate new training scenarios. This closed-loop feedback mechanism ensures training content remains current and relevant without manual intervention, allowing rapid adaptation to new threats.
Data Source
AI summary
A mission-based cyber training platform allows both offensive and defensive oriented participants to test their skills in a game-based virtual environment against a live or virtual opponent. The system builds realistic virtual environments to perform the training in an isolated and controlled setting. Dynamic configuration supports unique missions using a combination of real and/or virtual machines, software resources, tools, and network components. Game engine behaves in a manner that will vary if participant attempts to replay a scenario based upon alternate options available to the engine. Scoring and leader boards are used to identify skill gaps/strengths and measure performance for each training participant. A detailed assessment of a player's performance is provided at the end of the mission and is stored in a user profile/training record.