Log Entry Abnormality Detection via Baseline Fingerprinting
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Solution Overview
Problem
The detection of security and operational issues in computing devices is hindered by abnormalities in log entry collection, such as temporal, data type, and columnar inconsistencies, which are difficult to identify due to the vast volume of data and the need for manual inspection, making it impractical to detect anomalies in a timely manner.
Innovation Solution
The techniques analyze metadata of log entries to detect temporal, data type, and columnar inconsistencies, using baseline fingerprints generated from historical log entries to identify abnormalities in new log entries, allowing for automated detection and remedial actions without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of log entries is performed, then detection accuracy can be maintained, but the time required and resource consumption increase significantly
Solution Approach 1:
The patent creates a baseline fingerprint that is a simplified representation or copy of the normal log entry characteristics. This fingerprint serves as a reference model that can be quickly compared against new log entries without requiring full manual inspection, thus maintaining detection accuracy while reducing time consumption.
Solution Approach 2:
The patent replaces the mechanical process of manual log entry inspection with an automated computational system. The system uses algorithmic comparison between baseline fingerprints and current log entry fingerprints to detect abnormalities, substituting human manual review with automated processing that is both faster and scalable.
2Reliability
If comprehensive analysis of all log entries is performed, then detection reliability is improved, but processing complexity and resource requirements increase
Solution Approach 1:
The patent extracts only the essential characteristics of log entries to create a fingerprint representation. Instead of analyzing all log entry data in full detail, the system extracts key features (such as timestamp patterns, source identifiers, message types) that are sufficient for anomaly detection, thereby reducing processing complexity while maintaining reliability.
Solution Approach 2:
The patent transforms the log entry data from its original complex format into a simplified fingerprint representation with specific parameters. This parameter transformation allows the system to work with a reduced set of critical attributes rather than the complete log entry structure, simplifying processing while preserving detection capability.
3Productivity
If automated detection systems are implemented, then processing speed increases, but the ability to detect subtle abnormalities may decrease
Solution Approach 1:
The patent performs preliminary action by establishing a baseline fingerprint from normal log entries before actual anomaly detection begins. This baseline captures the expected patterns and characteristics of normal operations, enabling the automated system to quickly compare new entries against this pre-established reference, thereby maintaining sensitivity to subtle deviations while preserving high processing speed.
Data Source
AI summary
Log entries and baseline log entries have timestamps, and can be structured over columns of respective data types. Temporal inconsistency can be identified by comparing a probability distribution of time differences between the timestamps of the log entries with a probability distribution of time differences between the timestamps of the baseline log entries. Data type inconsistency can be identified by comparing a data type of each column of the log entries with a data type of a corresponding column of the baseline log entries. Columnar inconsistency can be identified by comparing a number of the columns of the log entries with a number of the columns of the baseline log entries. In response to identification of temporal, data type, and/or columnar inconsistency, that an abnormality exists in collecting the log entries is detected.