| --- |
| license: mit |
| language: |
| - en |
| metrics: |
| - accuracy |
| library_name: keras |
| tags: |
| - cybersecurity |
| - phishing-detection |
| - qr-code-analysis |
| --- |
| |
| # AEGIS-SWARM: Visual Phishing Auditor |
| **Developer:** Muhammad Abdullah |
| **Institution:** COMSATS University Islamabad, Lahore Campus |
|
|
| ## Overview |
| This model is a **Convolutional Neural Network (CNN)** developed as part of the AEGIS-SWARM multi-modal threat triage system. It is specifically designed to analyze images (such as QR codes) to determine if they lead to malicious phishing sites. |
|
|
| ## Technical Specifications |
| - **Architecture:** Sequential CNN (Conv2D, MaxPooling, Dense layers) |
| - **Input Shape:** (128, 128, 3) |
| - **Framework:** TensorFlow/Keras |
|
|
| ## Training Progress |
| The model was trained on the **CIC-Trap4Phish** dataset, involving over **1.5 million images**. |
| - **Epochs:** 5 |
| - **Final Accuracy:** 63.61% |
| - **Final Loss:** 0.5798 |
|
|
| ## Usage |
| To use this model in your own Python environment: |
| ```python |
| from tensorflow.keras.models import load_model |
| from huggingface_hub import hf_hub_download |
| |
| # 1. Download the weights |
| model_path = hf_hub_download(repo_id="Wa1106/AEGIS-SWARM-Visual-Agent", filename="visual_agent_v1.h5") |
| |
| # 2. Load the model |
| model = load_model(model_path) |
| |
| # 3. Predict |
| # results = model.predict(your_preprocessed_image) |
| \``` |
| --- |
|
|
