Abstract
This project proposes Anomaly Detection in Logs, a research initiative aimed at AI & Data Science learners. It combines Isolation Forest, Elasticsearch to deliver a functional prototype while reinforcing fundamentals. Budget and duration are estimates; actual costs vary by location and component choices.
Problem statement
Students in AI & Data Science often lack hands-on project ideas that connect theory with practice. Anomaly Detection in Logs addresses a real-world need by applying Isolation Forest, Elasticsearch to solve a domain-specific problem.
Objectives
- Understand the core concepts behind Anomaly Detection in Logs.
- Design and implement a working prototype using Isolation Forest, Elasticsearch.
- Evaluate performance and usability of the solution.
- Document findings and suggest improvements.
Key features
- Core functionality for Anomaly Detection in Logs
- User-friendly interface or control mechanism
- Data logging or reporting where applicable
- Modular and extensible design
Implementation roadmap
- Research and requirement analysis
- System design and architecture
- Implementation of core modules
- Integration and testing
- Documentation and final report
Required hardware & software
- Laptop/PC
- Development IDE
- Required libraries and frameworks
Testing approach
Unit testing of individual modules, integration testing, and a basic usability/performance check. No fabricated experimental results are claimed.
Expected outcome
A functional research prototype demonstrating the feasibility of Anomaly Detection in Logs, along with documentation.
Limitations
Prototype scope is limited; real-world deployment may require additional safety, scaling, and compliance considerations.
Future scope
Extend with advanced features, better UX, and integration with related systems.
Learning prerequisites
- Basic knowledge of Isolation Forest
- Basic knowledge of Elasticsearch