Another parser for server logs to find potential vulnerabilities
this project offers a simple dashboard to analyze server logs, identify top IP contributors, and detect peak traffic hours. It helps businesses monitor server health, detect anomalies, and optimize resource planning through quick, data-driven insights.
A powerful, web-based log analysis application designed for ISP and office environments. Built with modern web technologies to provide comprehensive log parsing, analysis, and visualization capabilities for Apache, Nginx, Syslog, and network device logs.
IntelliJ IDEA plugin for analyzing Apache/Nginx log files with traffic patterns, security analysis, and performance metrics
Web Interface with Drill Down Capability and Log Visualization integration for MySQL Schema `apache_logs` built with Express.js & Apache ECharts frameworks
A lightweight, cross-platform Python-based agent for collecting, parsing, and forwarding Apache web server logs via an API endpoint to security analytics platforms.
Automated log analysis pipeline using PySpark to simulate Azure Databricks workflows. This project parses raw Apache logs, identifies operational patterns and anomalies using SQL queries, and exports results for visualization in Tableau — reducing manual review time in log analysis.
A PHP script that filters known IP addresses from an Apache HTTP log file
Web server logs analyzer
Repository created to generate awareness of apache-logs-to-mysql & mysql-to-apache-echarts repositories in Search Engines
Splunk project analyzing simulated Apache web logs to detect failing endpoints, access trends, slow APIs, suspicious patterns, and usage by device/browser. Includes complex SPL queries and visual storytelling.
Built a Go-based platform for real-time log processing, SQL data storage, and automated HTML/CSV reporting with concurrency, scheduling, and alerting capabilities.
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