Probabilistic Cyber Operation Training for Unknown Network Topologies
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Solution Overview
Problem
Current cyber penetration testing systems are limited by the assumption of a known network topology and lack of detailed device state modeling, failing to account for variable network sizes and real-world dynamics, such as devices joining and leaving networks, leading to ineffective training and defense strategies.
Innovation Solution
A system that trains penetration testing systems by simulating variable-sized adversary networks, using a trainer device with a knowledge base and a quantification system to update action outcome probabilities based on actual penetration attempts by aggressor devices, enhancing the realism and effectiveness of training scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current penetration testing systems assume known network topology, then the system complexity is reduced and algorithms can be reasoned about, but the system fails to account for real-world dynamics where network topology is unknown and variable
Solution Approach 1:
The system transitions from static, predetermined network topology assumptions to dynamic, adaptive topology discovery. The penetration testing system continuously updates its understanding of network structure during operation, allowing it to adapt to unknown topologies while managing complexity through incremental learning rather than requiring complete topology knowledge beforehand.
Solution Approach 2:
The system performs preliminary reconnaissance actions to discover network topology before executing main penetration tasks. By conducting initial scanning and mapping operations, the system gathers topology information in advance, enabling subsequent algorithms to operate with better knowledge of the network structure without assuming complete prior knowledge.
2Measurement precision
If detailed device states are incorporated into probability calculations, then the accuracy of exploit success prediction improves, but the computational complexity increases
Solution Approach 1:
The system segments device state information into discrete, manageable categories (e.g., service running status, vulnerability presence, configuration settings) that can be independently evaluated. This segmentation allows the system to incorporate detailed device states into probability calculations without overwhelming computational complexity, as each segment can be assessed separately and combined through probabilistic reasoning.
Solution Approach 2:
The system dynamically adjusts the level of detail in device state parameters based on contextual needs and available information. Rather than always computing with maximum detail, the system adapts parameter granularity to balance accuracy requirements against computational resources, incorporating more detailed states when confidence is low and fewer states when confidence is high.
3Reliability
If variable-sized networks are simulated in training, then the robustness of penetration testing systems improves, but the training system complexity increases
Solution Approach 1:
The training system implements a universal simulated network environment that can dynamically configure itself to represent different network sizes and topologies using the same core infrastructure. Rather than maintaining separate training environments for each network configuration, the system uses parameterized network models that can be adjusted to simulate various scenarios, reducing overall system complexity while improving robustness.
Solution Approach 2:
The system creates simplified copies of real-world network characteristics in the training environment, capturing essential dynamics of variable network sizes without replicating every detail. These abstracted network models preserve the key behavioral patterns and uncertainties of real networks while being computationally tractable for training purposes.
Data Source
AI summary
A search agent training system includes a trainer device. The trainer device includes a trainer network simulation of variable size, which further includes at least one selectable action, at least one selectable node, and a trainer knowledge base, which further includes at least one selectable action outcome probability value. The trainer knowledge base is populated by a quantification system. The trainer device receives an incoming action message from a search agent device including a selected action and a selected node. Next, based upon: a resulting action outcome probability value, the selected action, and the selected node, a resulting observation and a resulting reward is sent to the search agent device. The trainer device blocks node count report messages to the search agent device from the trainer device.