Build a Security Information and Event Management (SIEM) dashboard that ingests logs from multiple sources via syslog or file parsing, analyzes events with correlation rules, and visualizes security incidents through an interactive web interface. This project teaches log aggregation, event correlation, data visualization, and demonstrates enterprise security monitoring platforms.
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Design the log ingestion pipeline by researching SIEM architectures and log collection methods: implement syslog receivers listening on UDP/TCP ports for incoming logs from network devices, parse log files from various sources (application logs, firewall logs, IDS alerts), and support multiple log formats through pluggable parsers. Create a normalized event schema so logs from different sources are stored in a consistent format with standard fields (timestamp, source, severity, event type, message).
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Build a FastAPI/Flask backend that receives ingested logs, performs initial parsing and normalization, and stores events in SQLite or PostgreSQL database. Implement REST API endpoints allowing the frontend to query events by various criteria (time range, source IP, event type, severity), retrieve statistics and aggregations, and trigger custom analyses. Add authentication and authorization so only authorized analysts can access events.
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Implement event correlation rules that detect patterns across multiple events indicating security incidents: create a rule engine matching sequences of events (e.g., "5 failed logins followed by successful login from unusual location" = suspicious activity), combining events by source IP/user/host, and calculating risk scores based on matched patterns. Store rules in a configuration format allowing security teams to create and modify detection rules without coding.
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Create a data visualization frontend using React, Chart.js, or Recharts to display security events and analytics: build dashboards showing event timelines, heatmaps of activity by source/destination, pie charts of event distribution by type/severity, and geographic maps showing attack origins. Implement filtering and drill-down capabilities allowing analysts to investigate specific events by exploring related logs.
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Build severity-based filtering and alerting that categorizes events by criticality (critical, high, medium, low, informational) based on event type and correlation results. Implement alert thresholds so critical events trigger immediate notifications through email, Slack, or webhook services. Create alert fatigue prevention by grouping related alerts and allowing analysts to tune sensitivity per event type.
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Implement time range analysis and trending allowing analysts to query events across configurable time windows (last hour, last 24 hours, last 7 days, custom ranges). Build trending visualizations showing event volume changes over time, identify anomalies where event rates deviate from baseline patterns, and detect escalating incidents where attack activity is increasing.
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Add forensic investigation capabilities enabling analysts to pivot between related events: click on a source IP to see all events from that IP, click on a user to see all their activities, view full event details with all available metadata, and export events in various formats (CSV, JSON) for external analysis. Implement search functionality allowing complex queries across multiple fields and text search within log messages.
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Build comprehensive documentation explaining SIEM concepts and log correlation methodology, providing deployment and configuration guidance, and including examples of detecting real security incidents through correlation. Discuss limitations (SIEM effectiveness depends on quality of ingested logs and rule quality), compare to commercial SIEM platforms (Splunk, Elastic Stack, ArcSight), and explain how SIEM fits into broader security operations centers (SOCs) and incident response workflows.
- Log aggregation and normalization
- Event correlation and pattern matching
- RESTful API design and backend architecture
- Data visualization and dashboarding
- Database design for log storage
- Alert thresholds and anomaly detection
- Security incident analysis
- Syslog receiver and log file parser
- Normalized event storage in database
- Event correlation rule engine
- FastAPI/Flask backend with REST APIs
- React frontend with interactive dashboards
- Severity-based alerting system
- Forensic investigation and pivot capabilities