AI Cyber Training With Adaptive Unit Attack Generation
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Solution Overview
Problem
Existing cyber training methods struggle to effectively simulate complex and diverse cyber attacks and defenses in dynamic ICT environments, leading to inaccuracies in predicting attack paths and generating realistic training scenarios.
Innovation Solution
An AI-based cyber training method and apparatus that generates and executes unit attacks using an attack agent trained on environment and tool information, determining success or defense strategies, and updates the environment to simulate realistic scenarios, with reinforcement learning to optimize attack sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional cyber range training with fixed attack and defense scenarios is used, then training structure is simple and easy to implement, but it cannot adapt to rapidly changing ICT environments and sophisticated attack tools
Solution Approach 1:
The patent implements dynamic training scenarios where attack graphs and defense strategies are automatically generated and updated based on current ICT environment data. The system transitions from static pre-defined scenarios to dynamic adaptive scenarios that evolve with changing threat landscapes and technological environments, allowing the training system to remain relevant without manual reconfiguration.
Solution Approach 2:
The system creates virtual copies of real-world attack scenarios and environments through synthesized training data. Instead of requiring actual complex ICT infrastructure, the patent generates realistic simulation environments that replicate attack patterns, network topologies, and defense mechanisms, enabling adaptable training without proportional increases in physical system complexity.
2Measurement precision
If attack graphs identify all possible attack paths, then recall of attack paths is improved, but accuracy of prediction decreases due to considering all paths without attacker capability constraints
Solution Approach 1:
The patent applies local quality by differentiating attack path analysis based on specific attacker capabilities, target vulnerabilities, and contextual factors. Instead of uniformly treating all attack paths equally, the system selectively emphasizes paths that are locally relevant to specific training scenarios and attacker profiles, improving prediction accuracy for realistic attack scenarios while maintaining comprehensive coverage when needed.
Solution Approach 2:
The system dynamically adjusts attack graph parameters such as edge weights, path probabilities, and node importance based on attacker capability models and vulnerability assessments. By changing these parameters according to specific training objectives and threat models, the system optimizes the balance between identifying all possible paths and predicting the most likely actual attack paths.
3Measurement precision
If Breach and Attack Simulation (BAS) technology is used to simulate multi-stage cyber attack actions, then realistic attack scenario simulation is improved, but it becomes difficult to establish defense strategies due to generalized attack sequences and virtual simulation limitations
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors simulation outcomes, attack effectiveness metrics, and defense performance data. This feedback is used to automatically refine attack sequences, update vulnerability models, and suggest optimized defense strategies. The feedback loop transforms the previously static BAS simulation into an interactive system that actively assists in defense strategy formulation based on observed attack patterns and outcomes.
Solution Approach 2:
The system performs preliminary analysis of attack scenarios, pre-identifying vulnerable points, potential attack paths, and effective defense measures before actual training exercises. By conducting preliminary simulations and generating pre-configured defense strategies, the system reduces the operational complexity during actual training while maintaining high simulation realism, as the heavy analytical work is completed in advance.
4Adaptability or versatility
If AI-based attack agent generates and executes unit attacks dynamically, then training realism and adaptability are improved, but computational resources and training time increase
Solution Approach 1:
The patent applies partial action by having the AI-based attack agent focus on generating and executing only the most critical or high-probability unit attacks rather than exhaustively exploring all possible attack sequences. The system identifies and prioritizes attacks based on vulnerability severity, attack success probability, and training objectives, executing a subset of attacks that provide maximum training value while reducing overall computational burden and training time.
Data Source
AI summary
Disclosed herein are an artificial Intelligence (AI)-based cyber training method. The AI-based cyber training method may include generating a unit attack by training an attack agent based on environment and state information of a cyber range (CR) and a set of attack tools executable on a system, executing the unit attack in the CR, and then determining whether the unit attack has succeeded, and determining whether to perform an attack or a defense based on whether the unit attack has succeeded.