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Log Analysis and Anomaly Detection Dashboard

Approx. component budget: Rs. 0 - 1,500

Objective

Turn raw logs into useful warnings so problems are found before users complain.

What you will build

A pipeline that collects server logs, parses them, finds unusual events such as many failed logins, and shows them on a live dashboard with alerts.

Results you can expect

• Logs processed per second
• Detection of injected brute-force and traffic-spike events
• Alert delay
• False alert count per day
• And dashboard clarity tested with 5 users

What you will learn

Log parsing, time-series anomaly detection, alert design, and basics of the ELK stack.

Tools and notes

Python, Elasticsearch/Kibana or Grafana Loki, Filebeat, scikit-learn (Isolation Forest). Use generated or your own server logs only.

Resume points from this project

• Built and tested Log Analysis and Anomaly Detection Dashboard; objective: turn raw logs into useful warnings so problems are found before users complain.
• Measured and analysed logs processed per second and detection of injected brute-force and traffic-spike events; wrote the report and presented the results in a viva. Add your own numbers: [x].

Replace [x] with your own measured results. Write only what you really did.

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