
- by x32x01 ||

The Challenge:
Digital forensics investigators typically spend 60-70% of their time just parsing and correlating log files from different sources - Apache, Windows Event Logs, Syslog, IIS, and more. Each format requires different parsing techniques, and creating a coherent timeline manually can take days or weeks.The Solution:
I've developed a comprehensive web-based Digital Evidence Timeline Analyzer that transforms how investigators approach log analysis:Key Capabilities:
• Multi-Format Parsing: Automatically detects and parses Apache, Nginx, Windows Event Logs, Syslog, IIS, and generic formats
• Interactive Timeline Visualization: Creates chronological timelines showing event patterns and anomalies
• Intelligent Threat Detection: Automatically flags suspicious activities like failed logins, privilege escalation attempts, and SQL injection patterns
• Advanced Filtering: Filter by time ranges, keywords, severity levels, and IP addresses
• Professional Reporting: Generate court-ready reports in JSON, CSV, and HTML formats
• Zero Infrastructure: Runs entirely in the browser - no servers, databases, or cloud dependencies required.
• The tool is completely free.
Real-World Impact:
This tool reduces investigation time from weeks to hours by:- Automatically correlating events across multiple log sources
- Highlighting critical security incidents with severity-based color coding
- Providing exportable evidence chains for legal proceedings
- Enabling pattern recognition that might be missed in manual analysis
Perfect for:
Digital forensics investigators
Incident response teams
Security analysts
Law enforcement cyber units
Corporate security teams
What makes this different?
Unlike expensive commercial tools that require complex setup and licensing, this solution is immediately accessible to any investigator with a web browser. It democratizes advanced forensics capabilities for organizations of all sizes.Digital evidence analysis shouldn't be a bottleneck in justice. This tool ensures that critical evidence is identified, analyzed, and documented efficiently and accurately.
Try it here: https://moe-code-22.github.io/digital-forensics/