AI Virtual Machine for Dynamic Network Topology Discovery
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Solution Overview
Problem
Accurately understanding and modeling the topology of computer networks, especially enterprise networks, is challenging due to their dynamic nature, which hinders effective analysis and security measures.
Innovation Solution
Implementing a virtual machine that uses AI, data mining, and machine learning algorithms to learn and model the internal network topology, creating fictitious identities and patterns to enhance security and detect threats while maintaining read-only access to the directory server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network analysis methods are used, then network analysis can be performed, but accurate understanding of dynamic network topology is difficult to achieve
Solution Approach 1:
The patent implements a dynamic network topology discovery system that continuously adapts to changing network conditions. The system uses automated agents that periodically probe network devices and update topology models in real-time, transforming the static analysis approach into a dynamic one that maintains accuracy as the network evolves.
Solution Approach 2:
The system performs preliminary network discovery and topology mapping before security analysis or troubleshooting activities. By establishing an accurate baseline topology model in advance, the system enables more effective subsequent analysis and faster response to network events.
2Productivity
If network monitoring is implemented to track topology changes, then network analysis capability is improved, but network performance and visibility to authorized users may be disrupted
Solution Approach 1:
The patent introduces automated discovery agents as intermediaries between the network infrastructure and the analysis system. These agents perform topology discovery by sending probe packets and collecting device information without requiring direct intervention in normal network traffic flows, thus maintaining network operations while enabling comprehensive analysis.
Solution Approach 2:
The network monitoring function is segmented into separate automated agents that operate independently from the main network traffic. This allows topology discovery and security analysis to proceed in parallel with normal network operations, preventing disruption to authorized users while maintaining analytical capabilities.
Data Source
AI summary
Introduced here are techniques for modeling networks in a discrete manner. More specifically, various embodiments concern a virtual machine that collects data regarding a network and applies algorithms to the data to discover network elements, which can be used to discover the topology of the network and model the network. The algorithms applied by the virtual machine may also recognize patterns within the data corresponding to naming schemes, subnet structures, application logic, etc. In some embodiments, the algorithms employ artificial intelligence techniques in order to more promptly respond to changes in the data. The virtual machine may only have read-only access to certain objects residing within the network. For example, the virtual machine may be able to examine information hosted by a directory server, but the virtual machine may not be able to effect any changes to the information.