Ransomware Detection using Machine Learning | Python Django Project with Source Code 2026

Published: 20 August 2026
on channel: codeAj Marketplace

🛡️ RansomShield AI – Real-Time Ransomware Detection System (Python + Django + Machine Learning)

A working ransomware detection system that scores every running process from 0 to 100 — not by matching virus signatures, but by watching behaviour. File touch rate, entropy spikes, disk write volume, process age and open network connections all become a feature vector, which then passes through three ML models plus a bank of behavioural rules.

⚠️ IMPORTANT: This project contains zero ransomware code. Not a single line. The agent only opens files in read mode to sample bytes for entropy calculation — it never writes, encrypts, renames or deletes anything. For the demo, pre-generated synthetic telemetry is replayed through the exact same pipeline a live agent uses. Your examiner sees a CRITICAL incident on screen, and your laptop's files stay exactly where they were.

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🔥 KEY FEATURES
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✅ 0-100 Risk Score with a real explanation — a bar chart shows exactly which features drove the score (z-score against a benign baseline, weighted by model feature importance). Nobody has to take the number on faith.
✅ Three ML Models Blended — RandomForest + XGBoost for supervised classification, IsolationForest for anomalies it was never trained on, plus a side-by-side model comparison page.
✅ Built-in False Positive Test — watch "BackupAgent.exe" sit at LOW while "svchost32.exe" hits 100. That's the hardest problem in behavioural detection, demonstrated live.
✅ Standalone Endpoint Agent — needs only psutil, watchdog and requests. Auto-registers, stores its API key, and POSTs telemetry every 2 seconds.
✅ SOC-Style Dashboard — live process table, incidents filterable by severity and status, per-process telemetry history charts, and a full event timeline with rule hits.
✅ Safe Response Actions — ALERT and QUARANTINE only. No auto-terminate anywhere in the codebase, which is a design decision you can defend in your viva.
✅ Smart Email Alerts — one CRITICAL incident sends exactly one email. SMTP failures are caught and logged, and never break the detection pipeline.
✅ Real Evaluation Metrics — Accuracy, Precision, Recall, F1, ROC-AUC, false positive rate, backup-job-specific FPR and mean detection latency. Deterministic on seed=42, so your report numbers match what your guide sees.
✅ No Dataset Hunting — data_synth.py generates the entire training dataset for you. No searching Kaggle at 2 AM.

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🛠️ TECH STACK
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Python 3.10.11 | Django 5 | Django REST Framework | scikit-learn | XGBoost | SQLite3 | psutil | watchdog | Chart.js | Tailwind CSS

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🏗️ CLEAN 3-FOLDER ARCHITECTURE
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📁 detection/ → Zero Django dependency. Import it from anywhere.
📁 agent/ → Zero dependency on detection/ or Django, because in the real world you ship an agent to a thousand endpoints and you don't want scikit-learn on all of them.
📁 backend/ → Ties it together with a shared ingest path in core/services.py, so the demo path and the production path are genuinely the same code.

This is the part examiners tend to poke at, and this structure reads as "this person has actually thought about deployment."

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🎓 PERFECT FOR
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BCA • MCA • B.Tech CSE • M.Tech • BSc IT • Computer Science • Cyber Security • AI/ML • Data Science
Final Year Project • Semester Project • Mini Project • Internship Project • Academic Demonstration

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📦 WHAT YOU GET (₹499 only — MRP ₹1999)
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✔️ Complete source code (every Python file, migrations, management commands, scenario generator)
✔️ Trained model artefacts
✔️ Project report and documentation
✔️ Step-by-step installation guide
✔️ Chat support until your setup is done

⏱️ Setup to a populated dashboard takes roughly 25 minutes if Python is already installed.
💻 8GB RAM and no GPU? It runs fine. All three models are CPU-based, the database is SQLite3, and there's no Docker or Postgres to fight with.

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🛒 GET THIS PROJECT NOW
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