Automated Network Training Evaluation via Dynamic Attack Adaptation
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Solution Overview
Problem
Current computer-based training exercises for network defense lack automation, requiring significant manual evaluation and supervision, and do not effectively simulate realistic cyber attack scenarios, limiting the training experience for network administrators and other professionals.
Innovation Solution
A virtual machine-based training environment with a control and monitoring system, an attack system, and a target system that automatically initiates and responds to attacks, allowing for dynamic adaptation and evaluation of training scenarios, enabling both small-scale and large-scale exercises, including 'free play' activities, with automated evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual evaluation and supervision is used in training exercises, then instructor control and supervision is improved, but automation and efficiency deteriorates
Solution Approach 1:
An automated evaluation system acts as an intermediary between trainees and instructors. The system includes evaluation modules that automatically assess trainee actions, generate performance reports, and provide feedback, while instructors retain supervisory control through configuration and oversight capabilities.
Solution Approach 2:
The training system performs self-evaluation of trainee actions through automated monitoring and assessment mechanisms. The system automatically tracks trainee activities, evaluates performance against predefined criteria, and generates feedback without requiring continuous manual intervention.
2Adaptability or versatility
If large-scale group exercises are conducted, then training realism and interaction are improved, but complexity and supervision requirements worsen
Solution Approach 1:
The training system is divided into modular components including scenario management modules, evaluation modules, communication modules, and configuration modules. Each module handles specific aspects of complex training scenarios, allowing large-scale exercises to be managed through coordinated independent functions.
Solution Approach 2:
The training system is designed to support multiple types of training scenarios (cyber attacks, natural disasters, terrorism) and various group sizes through universal configuration options. A single system architecture can adapt to different training requirements through programmable parameters and configurable settings.
3Ease of operation
If small-scale training exercises are used, then automation and ease of use are improved, but training realism and interaction deteriorates
Solution Approach 1:
The training system dynamically adjusts its complexity and scope based on configuration parameters and training objectives. The same system can operate in simplified mode for individual training or expand to support complex multi-person scenarios, adapting its behavior to match the required training scale.
4Productivity
If automated evaluation is implemented, then productivity and efficiency are improved, but measurement precision and evaluation accuracy worsen
Solution Approach 1:
The automated evaluation system incorporates feedback mechanisms where evaluation results are continuously refined based on instructor input and trainee performance data. The system learns from evaluated scenarios and adjusts evaluation criteria to improve accuracy while maintaining high processing efficiency.
Data Source
AI summary
This disclosure generally relates to automated execution and evaluation of computer network training exercises, such as in a virtual machine environment. An example environment includes a control and monitoring system, an attack system, and a target system. The control and monitoring system initiates a training scenario to cause the attack system to engage in an attack against the target system. The target system then performs an action in response to the attack. Monitor information associated with the attack against the target system is collected by continuously monitoring the training scenario. The attack system is then capable of sending dynamic response data to the target system, wherein the dynamic response data is generated according to the collected monitor information to adapt the training scenario to the action performed by the target system. The control and monitoring system then generates an automated evaluation based upon the collected monitor information.