Autonomous Network Configuration Management via Threat Modeling
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Solution Overview
Problem
Managing complex and distributed networks, especially for large corporations or financial institutions, becomes increasingly difficult due to varying configurations, security policies, and changing threat levels, requiring an efficient centralized implementation model.
Innovation Solution
The system employs a probe component to gather information about network elements, a diagnosis component to learn configurations, and a modeling component to develop a threat model using machine learning techniques, which compiles data to identify vulnerabilities and deploy configurations to resolve them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If networks continuously add network elements with different configurations and security policies, then network functionality and coverage are improved, but network complexity and difficulty of management increase
Solution Approach 1:
The patent segments the network management system into distinct functional components: a probe component for discovering network elements, a diagnosis component for analyzing configurations, and a modeling component for developing threat models. This segmentation allows each component to handle specific tasks independently, managing complexity while supporting network growth and diversity.
Solution Approach 2:
The patent introduces an intermediary autonomous agent that mediates between network elements and security policies. This agent automatically discovers network elements, learns their configurations, and applies appropriate security policies without requiring manual management of each element, thus handling network complexity while maintaining adaptability.
2Reliability
If manual management of each network element is attempted, then configuration control is maintained, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The patent implements self-service through autonomous agents that automatically probe network elements, discover their configurations, and apply security policies without human intervention. The system serves itself by continuously monitoring and adapting to network changes, maintaining configuration control while eliminating time-consuming manual management tasks.
Solution Approach 2:
The patent performs preliminary actions by proactively discovering and analyzing network elements before security threats arise. The probe and diagnosis components continuously gather information about network elements in advance, enabling the system to prepare and apply appropriate security configurations before vulnerabilities are exploited, thus maintaining control while reducing response time.
3Productivity
If centralized configuration model is implemented, then management efficiency is improved, but system complexity and learning requirements increase
Solution Approach 1:
The patent creates a universal centralized management system that handles multiple network element types and configurations through a single autonomous agent framework. This agent can probe, diagnose, and model various network elements (routers, switches, servers, endpoints) using the same core mechanisms, improving management efficiency while avoiding the complexity of separate management systems for each element type.
4Reliability
If traditional security policy implementation is used, then policy enforcement is achieved, but adaptability to changing threat levels deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the autonomous agent continuously monitors network configurations, identifies vulnerabilities, and adjusts security policies based on learned information and changing threat models. The system uses feedback from the probe and diagnosis components to dynamically update security configurations, maintaining reliable policy enforcement while adapting to evolving threats through continuous learning and adjustment.
Data Source
AI summary
The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of autonomous asset configuration modeling and management. The innovation includes probing elements of a networked architecture to compile information about elements in the networked architecture. The innovation learns a configuration for the at least one element in the environment based on the probing and determines vulnerabilities in the learned configuration. The innovation develops a threat model based on the learned configuration. The innovation applies the threat model to the elements of the networked architecture and deploys a configuration that resolves the vulnerabilities based on the threat model to the elements in the networked architecture. The threat model can be developed over time using machine learning concepts and deep learning of data sources associated with the elements and vulnerabilities.