Cognitive Automation Framework for Explainable Network Security
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Solution Overview
Problem
Machine learning systems in computer networks operate as 'black boxes,' making it difficult for users to understand detected issues, and struggle with combinatorial explosion problems, leading to unsolvable issues in various domains.
Innovation Solution
A cognitive automation framework using a deep fusion reasoning engine (DFRE) that maintains a metamodel with layers from sub-symbolic to symbolic spaces, tracks updates over time, and provides explainable decisions by leveraging machine learning techniques for cognitive reasoning in networking, security, and IoT applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning systems are used for network analysis, then pattern detection capability is improved, but system explainability deteriorates due to black box operation
Solution Approach 1:
The patent segments the machine learning system into multiple interpretable components including decision trees, rules, and models that can be individually understood and explained. This segmentation allows the system to maintain high pattern detection capability while providing transparent explanations for each detection decision through separate, interpretable analysis modules.
Solution Approach 2:
The patent introduces an intermediary explanation layer that translates complex machine learning outputs into human-understandable formats. This intermediary component acts as a mediator between the black box algorithm and users, preserving the sophisticated pattern recognition capabilities while adding a layer of interpretability through visualizations, natural language explanations, and structured reasoning displays.
2Productivity
If machine learning systems automatically initiate corrective measures, then network management efficiency is improved, but user control and understanding deteriorate
Solution Approach 1:
The patent implements comprehensive feedback mechanisms that provide users with detailed information about automated corrective measures before they are executed. The system offers explanations for detected issues, proposed solutions, and expected outcomes, allowing users to review and understand the reasoning behind automated actions while maintaining efficient network management through streamlined approval processes.
Solution Approach 2:
The patent performs preliminary analysis and explanation generation before automated corrective measures are initiated. The system prepares detailed reports, identifies potential issues, and presents recommended actions in advance, giving users time to review and understand the proposed changes before they are automatically implemented, thus maintaining both efficiency and user control.
3Loss of information
If cognitive learning systems are used to replicate human thinking, then decision explainability is improved, but system complexity increases
Solution Approach 1:
The patent segments the cognitive learning system into distinct functional layers including data processing, pattern recognition, reasoning, and explanation generation. Each layer performs a specific function and can be independently optimized and understood, reducing overall system complexity while maintaining high decision explainability through modular, interpretable components.
Solution Approach 2:
The patent employs universal cognitive processing mechanisms that can handle multiple types of network analysis tasks through a single integrated framework. This multi-functional approach uses general-purpose reasoning and explanation capabilities across different domains (security, performance, configuration), reducing the need for separate complex systems for each function while maintaining explainability.
Data Source
AI summary
In one embodiment, a device maintains a metamodel that describes a monitored system. The metamodel comprises a plurality of layers ranging from a sub-symbolic space to a symbolic space. The device tracks updates to the metamodel over time. The device updates the metamodel based in part on sub-symbolic time series data generated by the monitored system. The device receives, from a learning agent, a request for the updates to a particular layer of the metamodel associated with a specified time period. The device provides, to the learning agent, data indicative of one or more updates to the particular layer of the metamodel associated with the specified time period.