Log Alert System Server Log Recognition and Alert System

Harshali, Bobde and Avantika, Aglawe and Shruti, Lakhamapure and Dhanashri, Ukey and Prof. Komal, Dhakate (2024) Log Alert System Server Log Recognition and Alert System. International Journal of Trend in Scientific Research and Development, 8 (6). pp. 69-78. ISSN 2456-6470

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Abstract

Server logs are critical for the continuous monitoring of server infrastructure, capturing comprehensive details about system activities, errors, and user interactions. These logs provide invaluable data for assessing the performance, security, and health of server environments. However, the sheer volume and complexity of server logs, especially in large-scale deployments, render manual analysis time-consuming and error-prone. This creates a pressing need for automated solutions capable of real-time log recognition, anomaly detection, and alert generation.This paper introduces the design and development of the Server Log Recognition and Alert System (SLRAS), an intelligent, automated framework aimed at simplifying the management of server logs. By leveraging advanced log parsing techniques and machine learning algorithms, SLRAS efficiently processes vast amounts of log data, enabling the detection of critical server events, such as unauthorized access attempts, server crashes, resource exhaustion, and application errors. The system is capable of recognizing anomalous patterns within server logs, which could indicate potential security threats, performance bottlenecks, or system malfunctions. One of the key features of SLRAS is its real-time alerting mechanism, which provides instant notifications to server administrators. Alerts can be configured to be delivered via various communication channels, including email, SMS, or integrated dashboard notifications. This immediate feedback allows administrators to respond swiftly to potential issues, mitigating risks and reducing downtime. The system's customizable alert triggers offer flexibility, enabling administrators to define specific thresholds and conditions for alerts, ensuring relevance and reducing alert fatigue.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	06 Nov 2024 11:46
Last Modified:	06 Nov 2024 11:46
URI:	http://eprints.umsida.ac.id/id/eprint/14553

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