An Approach for Sytem Logs Analysis By Using Association Rule Mining

atul kumar shrivastava

Abstract


Recently a variety of data mining and machine learning algorithms are being used to analyze the information in the log ?les. A major road block for the ef?cient use of these algorithms is the inherent variability present in every log line of a log ?le. Each log line is a combination of a static message type ?eld and a variable parameter ?eld. Even though both these ?elds are required, the analyses algorithm often requires that these be separated out, in order to ?nd correlations in the repeating log event types. Log ?les contain valuable information about the execution of a system. This information is often used for debugging, operational pro?ling, ?nding anomalies, detecting security threats, measuring performance etc. The log ?les are usually too big for extracting this valuable information manually, even though manual perusal is still one of the more widely used techniques.which helps one to detect frequent patterns from log files, to build log file profiles, and to identify anomalous log file lines.


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