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Wall06/
AEGIS-SWARM-Visual-Agent

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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)
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Wall06/AEGIS-SWARM-Visual-Agent