Documentation
How to use the framework
Everything an analyst needs to prepare a dataset, run a detection and read the output — plus the API surface the frontend expects.
Supported file format
- • Extension: .csv
- • Encoding: UTF-8, comma delimited
- • Header row required
- • Maximum size: 25 MB
- • Column order must match the model feature schema
Upload instructions
- 1. Open a detection module from the navigation.
- 2. Drag your CSV onto the drop zone, or use Browse file.
- 3. Confirm the filename and size shown below the drop zone.
- 4. Press Analyze and wait for the service response.
Processing workflow
- 1. Client-side validation of type, size and emptiness.
- 2. Multipart upload to the FastAPI detection endpoint.
- 3. Feature extraction and model inference server-side.
- 4. JSON response rendered into metrics, charts and tables.
Result interpretation
Reading the output
Prediction
The winning class for the batch — an attack category or a malware family.
Confidence score
Probability assigned to the winning class, expressed as a percentage.
Risk / severity level
Derived triage priority: Low, Medium, High or Critical.
Recommendation
Suggested containment or mitigation action returned with the prediction.
Distribution charts
Share and absolute count of each predicted class across the batch.
Record table
Per-record predictions so individual flows or samples can be inspected.
API guide
Endpoints the frontend expects
Connected to API Backend
These routes describe the live contract that the dashboard uses to interact with the FastAPI machine learning service.
| Method | Endpoint | Purpose | Status |
|---|---|---|---|
| POST | /predict/network | Batch intrusion detection from a flow CSV | Live |
| POST | /predict/malware | Malware family classification from a feature CSV | Live |
| GET | /docs | API interactive documentation | Live |
| GET | / | Service health probe | Live |
FAQ