Build an Automated Log Analysis Tool for Cyber Incident Detection
Design a lightweight SIEM-like system that automatically scans log files to identify security incidents like brute-force attempts, privilege escalations, or suspicious access events — and generates alerts or reports.Log files from servers, apps, and firewalls hold valuable information for detecting cyber threats. Manual inspection is slow and error-prone. An automated tool helps quickly surface critical anomalies, allowing for faster response and forensic investigations.
This tool ingests log files, applies pattern matching and rule-based detection, flags events of interest, and sends alerts for suspicious activity. It can be tailored for web servers, SSH logs, or cloud events, supporting both batch and real-time log processing.
Log Ingestion & Preprocessing
Support input from `.log` files, syslogs, or APIs. Normalize logs into structured formats (e.g., JSON).
Rule-Based Anomaly Detection
Detect failed logins, unusual IPs, sudden privilege changes, and log injection attempts using regex or logic rules.
Incident Alert System
Send email/Slack alerts or trigger webhook actions when predefined thresholds or patterns are met.
Visualization & Report Generator
Display trends (e.g., top attackers, most failed logins) and allow users to download incident summaries.
Admins upload or stream log files to the tool. It parses each entry, applies detection logic (e.g., 5 failed logins in 1 minute), and raises flags on abnormal behavior. Alerts are sent in real time or summarized in periodic reports for SOC teams to review and respond.
- Input: Apache logs, SSH logs, or custom service logs.
- Parser normalizes logs by extracting key fields (IP, timestamp, status, method).
- Apply detection rules like IP blacklisting, brute-force heuristics, or sudden traffic spikes.
- Trigger alerts for high-severity events and track incident timelines.
- Allow report generation with event summaries, source IPs, timestamps, and incident types.
Log Parsing & Analysis
Python (re, json, loguru), Bash for log input, or ELK stack for advanced options.
Detection Rules
Regex, custom YAML/JSON rule sets, or integration with Sigma detection rules.
Alerting
Flask background job with email/Slack integration or use Celery for queue-based triggers.
Dashboard
React, Chart.js, or Streamlit for displaying alerts and incident analytics.
1. Build Log Input Pipeline
Allow users to upload `.log` files or use real-time streaming from syslog or webhooks.
2. Normalize Logs
Convert raw entries into structured JSON with fields like IP, status, URL, timestamp.
3. Implement Detection Logic
Create rules for brute-force detection, internal access violations, and file tampering alerts.
4. Add Alerts and Notifications
Configure alert channels and notification thresholds based on severity levels.
5. Build Visual Dashboard & Export Reports
Summarize alerts by source, frequency, and time range, and allow report downloads.
Make Logs Work for You — Not Against You
Build an automated log analysis tool to transform raw events into actionable insights and keep your systems ahead of cyber incidents.
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