THE HYBRID APPROACH USING TF-IDF,XG BOOST AND LIGHT GBM FOR LOG EVENT DETECTION

Syed Suriya Bhanu; M. Pavithra

THE HYBRID APPROACH USING TF-IDF,XG BOOST AND LIGHT GBM FOR LOG EVENT DETECTION

Authors: Syed Suriya Bhanu, M. Pavithra

Unique Paper ID: 175778
Volume: 11
Issue: 11
PageNo: 4672-4675

Keywords: No Keywords Found

Abstract:
The continuing growth of large-scale and complex software systems has led to growing interest in examining the possibilities of using the log files that were created during the runtime of the software. These files can be used for various purposes like error prediction, performance evaluation, learning of usage patterns, improving reliability, and so on. With software systems continuously becoming more and more complicated, the distinction of log files that were generated by different components of the software becomes a new task. The classification of log files is important for several reasons like resource optimization, compliance and auditing automation and analysis, or understanding the general system health. By classifying log files, organizations can better understand the health and performance of their systems. They can identify patterns, potential security threats, anomalies, errors, and malicious behaviors and storage can also be optimized. In the log files, each line represents a specific event that has occurred.

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ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 4672-4675

THE HYBRID APPROACH USING TF-IDF,XG BOOST AND LIGHT GBM FOR LOG EVENT DETECTION

Available:https://ijirt.org/Article?manuscript=175778

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