Large Network Simulation Using Synthetic Hosts
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Solution Overview
Problem
Simulating large-scale networks for training purposes is challenging due to the difficulty in replicating complex, real-world network behaviors and the high resource requirements, often resulting in unrealistic and costly traditional cyber training using small-scale networks.
Innovation Solution
A method and system that simulate large networks by instantiating data objects to respond to low-level network commands, using synthetic hosts of varying fidelities, including high, medium, and low fidelity hosts, to create a realistic training environment with minimal hardware resources, allowing for the simulation of millions of hosts and realistic network behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large-scale network is simulated using traditional methods, then the training realism is improved, but the resource consumption and cost increase significantly
Solution Approach 1:
The patent creates virtual copies of network hosts through data objects that simulate network behaviors without requiring physical hardware. Each data object represents a host with configurable characteristics (IP addresses, services, responses) that can be instantiated and destroyed as needed, providing realistic training scenarios with minimal resource consumption.
Solution Approach 2:
The system uses disposable data objects that can be quickly instantiated and destroyed. Background hosts are created on-demand with random characteristics and destroyed after serving their training purpose. This allows the simulation to scale to millions of hosts without permanent resource commitment, resolving the contradiction between training realism and resource consumption.
2Quantity of substance
If a small-scale network is used for training, then the resource consumption is reduced, but the training effectiveness and realism deteriorate
Solution Approach 1:
The patent implements dynamic network simulation where the network topology and host characteristics change over time. Background hosts are dynamically created with random IP addresses, services, and response behaviors. This dynamic nature allows a small physical infrastructure to simulate large-scale network conditions, maintaining training effectiveness while minimizing resource consumption.
Solution Approach 2:
The system changes parameters of simulated hosts dynamically, including IP addresses, service ports, response times, and behavioral characteristics. By varying these parameters across millions of virtual hosts, the system provides realistic training scenarios without requiring proportional physical resources, resolving the contradiction between resource consumption and training effectiveness.
3Ease of manufacture
If synthetic hosts with fixed characteristics are used, then the implementation is simplified, but the training value decreases due to lack of discrimination difficulty
Solution Approach 1:
The patent applies local quality by giving different data objects different characteristics tailored to their role. Background hosts have random IP addresses, services, and response behaviors that differ from each other and from target hosts. This local differentiation creates realistic discrimination challenges for trainees while maintaining implementation simplicity through programmatic generation of these varied characteristics.
Data Source
AI summary
Systems, methods, and apparatuses are described for simulating a network. Interrogations directed to hosts in the simulated network may be received. For some interrogations, data objects may be instantiated to simulate the interrogated hosts by, e.g., providing responses to low-level network commands. One or more characteristics of a simulated host may be determined randomly or pseudo-randomly.