JPCERTCC/LogonTracer

Investigate malicious Windows logon by visualizing and analyzing Windows event log

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JPCERTCC/LogonTracer has 3k+ stars and 450+ forks on GitHub, is written in Python, and was last pushed on Aug 2, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 22 minutes ago
Added to GitGenius on September 21st, 2026
Created on November 24th, 2017
Open Issues & Pull Requests: 22 (+0)
GitHub issues: Enabled
Number of forks: 493
Total Stargazers: 3,290 (+0)
Total Subscribers: 133 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 26.4 days
Mean response time: 26.4 days
90th percentile: 26.4 days
Tracked items: 1

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Issue Activity (beta)

Open issues: 4
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 573 days
Stale 30+ days: 4
Stale 90+ days: 3

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

Logontracer is a digital forensics and incident response tool that visualizes and analyzes Windows event logs to investigate malicious logon activity.

The tool addresses the challenge of manually parsing and correlating large volumes of Windows logon events by constructing a visual graph representation of authentication chains and lateral movement patterns. It ingests Windows event log data and builds relationships between logon events, user accounts, and systems to reveal attack paths that would be difficult to spot in raw log files.

Security teams conducting incident response investigations should consider this tool when they need to quickly understand how an attacker moved through a Windows environment. It suits organizations with Active Directory deployments where logon event analysis is central to threat investigation. The tool is particularly valuable for blue teams working through forensic timelines where authentication patterns are key evidence of compromise or persistence.

The project shows consistent maintenance with regular updates to address issues and improve functionality. Development activity demonstrates responsiveness to bug reports and feature requests from users. The codebase receives ongoing refinement to enhance the analysis capabilities and user experience of the visualization interface.