Neural Log Encoding With Unified Embeddings for Anomaly Detection
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Solution Overview
Problem
Existing log analysis techniques face challenges due to non-standardized recording practices across different systems, leading to inefficiencies in automating parsing and analyzing logs, which requires significant memory, time, and computing resources, and are not easily generalizable across various tasks.
Innovation Solution
A neural network-based approach that encodes log messages using transformer encoders and contrastive learning to produce unified representations, enabling efficient encoding and classification without task-specific labels, and combines log data with telemetry information for enhanced anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional log parsing and analysis techniques are used, then log information can be extracted and analyzed, but significant memory, time, and computing resources are required
Solution Approach 1:
The patent replaces traditional mechanical log parsing systems with a neural network-based encoding system. The neural network automatically encodes log messages into unified representations, eliminating the need for complex rule-based parsing and manual analysis workflows, thereby reducing computing resource requirements while maintaining analysis accuracy.
Solution Approach 2:
The patent transforms log data from raw text format into encoded vector representations through neural network processing. This parameter transformation changes the data structure from unstructured text requiring extensive parsing to structured embeddings that can be efficiently processed and analyzed, improving both speed and resource efficiency.
2Adaptability or versatility
If traditional log analysis methods are applied, then task-specific analysis can be performed, but the methods are not easily generalizable across various tasks and require task-specific labels
Solution Approach 1:
The patent creates a universal log encoding system using neural networks that produces task-agnostic representations. The same encoding model can be applied across multiple tasks (anomaly detection, classification, prediction) without requiring task-specific customization or labels, enabling one system to serve multiple purposes efficiently.
Solution Approach 2:
The patent performs preliminary encoding of log messages into unified representations before specific tasks are applied. This pre-processing step creates standardized embeddings that can be directly used across different tasks without requiring additional task-specific processing or label preparation, simplifying the overall workflow.
3Reliability
If logs from different systems are analyzed using traditional methods, then system-specific log patterns can be captured, but non-standardized recording practices make automation challenging
Solution Approach 1:
The patent replaces rule-based parsing mechanisms with neural network-based encoding. The neural network learns to handle non-standardized log formats automatically through training, capturing system-specific patterns without requiring manual rule creation for each log format, thereby improving ease of operation while maintaining reliability.
Data Source
AI summary
Methods, systems, and machine-readable mediums to perform a neural network to encode log data. In at least one embodiment, a processor comprising one or more circuits to encode at least one log message, at least in part, by encoding a first type of information in the at least one log message to obtain a first encoding, encoding a second type of information in the at least one log message to obtain a second encoding, and obtaining a resultant encoding at least in part by combing at least the first and second encodings.