Implementation of Malware Detection Using Artificial Intelligence Student: Andre · Module: CI601 · Academic Year: 2025 / 2026
Once deployed to Render (see DEPLOYMENT_GUIDE.md), your app will be live at:
https://malwareai.onrender.com
No local setup needed — just open the URL in any browser, anywhere, anytime.
| File | Purpose |
|---|---|
FYP_Report_APLUS_FINAL.docx |
Main FYP report (10 chapters, 47 pages, ~9,847 words) |
FYP_Report_APLUS_FINAL.pdf |
PDF backup of the report |
MalwareAI_Project_Board.html |
Trello-style project board (open in browser) |
DEPLOYMENT_GUIDE.md |
Step-by-step guide to deploy online (no VS Code needed) |
| File | Purpose |
|---|---|
Procfile |
Tells Render/Heroku how to start the app |
render.yaml |
One-click Render config |
runtime.txt |
Pins Python version (3.11.9) |
requirements.txt |
Python dependencies (now includes gunicorn) |
.gitignore |
Files to exclude from Git uploads |
| Folder / File | Contents |
|---|---|
src/ |
Python source: preprocess, train, evaluate, predict |
app/ |
Flask web application (production-ready) |
config.py |
Centralised configuration |
run_pipeline.py |
One-command training pipeline |
docs/ |
Screenshots and reference images |
results/ |
Evaluation graphs + metrics.json |
models/ |
Trained .pkl model files |
data/ |
NSL-KDD dataset |
Follow DEPLOYMENT_GUIDE.md to get a permanent live URL on Render.
- ✅ Free forever
- ✅ Works on any device with a browser
- ✅ No VS Code or local setup
- ✅ Examiner can access anytime
pip install -r requirements.txt
python run_pipeline.py # Train all 3 models (one time)
python app/app.py # Launch at http://localhost:5000| Model | Accuracy | F1 Score | AUC-ROC |
|---|---|---|---|
| Random Forest | 99.90% | 99.89% | 1.0000 |
| XGBoost (best) | 99.92% | 99.91% | 1.0000 |
| MLP Neural Network | 99.39% | 99.34% | 0.9998 |
Trained on 100,778 records · Evaluated on 25,195 unseen test records · NSL-KDD dataset