Deep Learning System for Anomalous Text Diagnostics
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Solution Overview
Problem
In large computer networks, isolating failure logs from a vast volume of unstructured text data generated by networking devices is cumbersome due to the data's size, distribution, and lack of a predefined structure, making it challenging to identify anomalies and diagnose issues efficiently.
Innovation Solution
A deep learning system that classifies anomalous portions of unstructured text by training machine learning-based models to predict text generated by specific commands or processes, highlighting anomalies within the text for display, thereby aiding in automated diagnostics and network operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used to isolate and analyze failure logs from unstructured text data, then diagnostic accuracy can be maintained, but the time and effort required increases significantly due to the large volume and distribution of logs
Solution Approach 1:
The patent replaces manual mechanical analysis methods with an automated deep learning system that uses neural networks to classify and identify anomalous portions of unstructured log data. The system automatically processes large volumes of distributed logs from multiple networking devices, eliminating the need for manual log isolation and analysis while maintaining high diagnostic accuracy through trained classification models.
2Productivity
If the volume of unstructured log data increases to support larger network scales, then network capacity improves, but the difficulty of searching and isolating relevant failure information increases
Solution Approach 1:
The deep learning system automatically processes and classifies unstructured log data from large-scale networks, replacing manual searching and isolation methods. The neural network models are trained to identify patterns and anomalies in log data, enabling efficient detection of failure information even as network capacity and log volume increase to support larger numbers of users and devices.
Solution Approach 2:
The system changes the approach to log analysis by transforming unstructured text data into structured classifications through deep learning models. The neural networks process log data with varying parameters such as log source, time, severity, and content patterns, automatically identifying relevant failure information without requiring manual adjustment of search parameters as network scale changes.
3Reliability
If unstructured log data is collected from multiple distributed sources, then comprehensive monitoring is achieved, but the complexity of data management and analysis increases
Solution Approach 1:
The deep learning system serves multiple functions simultaneously: it collects, classifies, analyzes, and identifies anomalies in log data from multiple distributed networking devices. The unified neural network architecture processes diverse log formats and sources through a single system, reducing management complexity while maintaining comprehensive monitoring coverage across the entire network infrastructure.
Data Source
AI summary
In one embodiment, an apparatus obtains unstructured text generated by a device regarding operation of the device. The apparatus identifies the unstructured text as associated with a particular command or process that generated the unstructured text. The apparatus classifies a portion of the unstructured text as anomalous by inputting the portion of the unstructured text to a machine learning-based model trained to predict text generated by the particular command or process. The apparatus provides provide the unstructured text for display that includes an indication that the portion of the unstructured text is anomalous.