B.Tech DDoS Detection System Final Year Project Report

DDoS Detection & Forensic Log Management System is a production-ready Django web application built for detecting DDoS attacks from uploaded network traffic CSV datasets. The system uses a Machine Learning model to classify traffic as Benign or DDoS, generates tamper-evident forensic logs for detected attacks, creates SHA-256 evidence hashes, stores forensic hashes on a custom blockchain ledger, and allows users to verify the integrity of evidence and blockchain records.

The platform provides two separate role-based consoles: Admin Panel and Security Analyst Panel. Admin users can manage users, datasets, ML model training, forensic logs, blockchain blocks, integrity checks, activity logs, brand settings, and reports. Security Analysts can upload datasets, validate traffic CSV files, run DDoS detection, view detection history, inspect DDoS events, verify forensic evidence, view blockchain records, and export reports.

This project is suitable for B.Tech, M.Tech, BCA, MCA, BE, ME, BSc, and MSc students who need a practical final year project, major project, or minor project based on cybersecurity, DDoS detection, machine learning, network traffic analysis, digital forensics, blockchain ledger, forensic evidence management, Django development, CSV data processing, Random Forest classification, SHA-256 hashing, admin dashboard, analyst panel, and report generation. FileMakr can provide this project with source code, project report, documentation, and setup support for academic submission.