JPCERTCC/LogonTracer

Features

• Authentication Pattern Analysis - Tracks user authentication patterns within system logs to identify security risks and potential account compromises.
• Forensic Log Auditors - Provides a dockerized security auditor for managing isolated investigation cases and performing forensic log analysis.
• Relational Visualizations - Maps account names and network addresses into a graph to visually identify patterns of system compromise.
• Hidden Markov Model Detection - Utilizes hidden Markov models and statistical algorithms to identify suspicious behavioral patterns across hosts and accounts.
• Graph Databases - Utilizes a relational graph database to map accounts and network addresses for compromise pattern visualization.
• Graph-Relational Databases - Maps account and network data into a graph database to analyze complex authentication paths.
• User Behavior Analysis - Applies statistical analysis, graph algorithms, and Markov models to identify suspicious user and host behavior.
• Rule-Based Detection Engines - Evaluates imported event data against standardized Sigma rule sets to identify malicious activity.
• Behavioral Analysis Engines - Applies graph algorithms and hidden Markov models to detect anomalous security behavior among hosts and accounts.
• User Behavior Anomaly Detection - Uses mathematical models and ranking algorithms to identify suspicious host and user account behavior.
• Sigma Rule Matching - Evaluates event logs against standardized Sigma rule sets to identify known malicious activity.
• Threat Relationship Visualizations - Maps account names and network addresses into a graph to visually identify system compromise patterns.
• Event Logging - Converts raw event logs into searchable graph databases to enable relational analysis and security investigations.
• Anomaly Detection - Uses mathematical models and ranking algorithms to identify suspicious hosts and accounts within event logs.
• Log Ingestion - Ingests raw event data into a graph database to enable complex relational security analysis.
• Threat - Scans imported logs against standardized Sigma rule sets to identify known malicious activity.
• Case-Isolated Storage - Implements independent data silos for separate security investigation cases to maintain strict access control.
• AI-Powered Threat Detection - Provides an AI-powered engine that assesses risk against known attack tactics to generate automated detection rules.
• Investigation Case Management - Provides isolated investigation databases with per-user access controls to maintain separate security audits.
• Event Log Importing - Converts raw log data into a searchable database using custom timezones and date filters.
• Log Analysis Tools - Visualizes and analyzes Windows logon events.
• Windows Artifact Analysis - Visualizes and analyzes Windows logon activity.
• Digital Forensics - Tool for visualizing and analyzing Windows logon events.
• Incident Response Frameworks - Visualizes and analyzes Windows event logs to investigate malicious logons.
• Windows Artifact Analysis - Tool for visualizing and analyzing Windows logon events.

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