Abstract: Log analysis is the systematic process of collecting, interpreting, and analysing log data generated by various systems, applications, devices, and networks. Logs are automatically produced records that document system events, user actions, errors, performance metrics, security incidents, and other activities critical to the functioning of IT environments. Through log analysis, organizations can gain valuable insights that enhance operational efficiency, bolster security, and ensure regulatory compliance. The core goal of log analysis is to convert raw log data into actionable information that helps in troubleshooting issues, identifying performance bottlenecks, detecting security threats, and optimizing system resources.
One of the primary motivations for log analysis is its utility in troubleshooting and diagnostics. Logs capture comprehensive details about system events, errors, crashes, and service disruptions, which are essential for identifying the root causes of issues. By analysing logs, IT administrators can gain a better understanding of how systems behave under normal and abnormal conditions. This enables them to pinpoint the exact causes of failures or performance degradations, allowing for timely resolution of problems and reducing system downtime. Furthermore, log analysis supports proactive monitoring by enabling real-time detection of anomalies, such as unusual spikes in resource usage, errors, or service response times. This helps organizations identify potential problems before they impact end user.
Keywords: Log Analysis, Machine Learning, NLP, Random Forest Algorithm, Root Cause Analysis, Visualization, Log Parsing.
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DOI:
10.17148/IJARCCE.2025.14645
