A web-based investigative platform that transforms complex telecommunications IPDR data into actionable intelligence through ML-powered anomaly detection.
This platform ingests heterogeneous IPDR logs, constructs communication graphs, and applies ML-based anomaly detection (94.16% accuracy) to deliver interactive visualizations. It features multi-format support, real-time processing, and end-to-end encryption.
Manual analysis of massive IPDR data is inefficient and inaccessible to non-technical stakeholders, making it difficult to detect fraud and respond to security incidents quickly.
An integrated platform that automates data parsing, applies ML for anomaly detection, and provides interactive graph visualizations for intuitive investigation and reporting.
Multi-Format Data Upload
AI-Powered Anomaly Detection
Interactive 2D & 3D Graphs
Automated Relationship Mapping
Comprehensive Reports History
Search & Isolate Functionality
Architecture: A decoupled frontend and backend with a FastAPI serving ML models and a Next.js client for interactive visualizations.
This project was developed for the CIIS 2025 Hackathon to address the challenge of "Mapping A-Party to B-Party in IPDR Logs." I was part of a 5-member team, "Team Brigade," from VIT Bhopal University.
My Role: My primary responsibilities included developing the backend infrastructure with FastAPI, integrating the ML models, and designing the API for the frontend to consume.
# Clone the repository git clone https://github.com/sujeetgund/ipdr-graph-engine.git cd ipdr-graph-engine # Backend setup cd backend pip install -r requirements.txt uvicorn app.main:app --reload # Frontend setup (in a new terminal) cd frontend npm install npm start
I'm passionate about leveraging AI and graph technologies to solve complex data challenges. Let's discuss how we can build something impactful together.