🛡️ AegisNet – Deep Learning Intrusion Detection System

High-performance deep learning IDS for real-time network flow threat detection.

Name Origin: Aegis (pronounced ee-jis) is the mythic shield of Zeus, symbolizing impenetrable defense. Net denotes the protected network surface. AegisNet represents a shielded, intelligently monitored infrastructure layer.

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🚀 Features

• Automated Data Pipeline: Ingests and combines all 8 CIC-IDS-2017 CSV files (2.8M+ rows) into a unified matrix.
• Intelligent Sanitization: Cleans 1.7GB+ of data, safely handling inf and NaN for a model‑ready dataset.
• Smart Feature Selection: RandomForest ranks 78 raw features, programmatically selecting the Top 30 for efficiency.
• AutoML Model Tuning: KerasTuner (Hyperband) explores architectures to select a champion configuration automatically.
• High-Precision Detection: 99%+ accuracy using a calibrated decision threshold to reduce false positives (alert fatigue).
• Dual-Pipeline Architecture:
  1. main.py – Full training pipeline producing a saved model.
  2. predictor.py – Lightweight batch prediction over unseen flows.
• CSV Preview Modal (scrollable & resizable): macOS “Quick Look”–style floating modal; horizontally and vertically scrollable; draggable resize handle; neon border; closes via overlay click or ESC. Efficient for large datasets.
• Threat Insights Panel: Summarizes total flows, benign vs. threat counts, and threat percentage with charts.
• Real Trained Model Integration: Flask API loads models/aegisnet.keras + scaler for live batch and single-flow inference.
• Threat Log Export: One-click CSV export with row_number, predicted_label, confidence_score for audit and forensics.
• System Console Log: Live operational event output for transparency and debugging.
• Flask API Integration: Local REST endpoints for prediction and UI interaction.
• Optional Theme Toggle (Neon ↔ Magenta): Palette switching via CSS variables and cinematic overlay transition.
• Manual Flow Analyzer: Centered form accepts top-feature inputs; /predict_single provides real-time inference; displays Benign/Threat with confidence percentage and a green/red border glow.

Dynamic Dual Theme System

• Toggle in the top-right corner switches between Neon Green + Cyan and Magenta + Blue + Black palettes.
• A 300 ms animated holographic overlay sweep creates a cinematic reboot effect during theme transition.
• The theme applies globally: panels, buttons, modals, charts, scrollbars, and text glow effects.
• Optional Audio Feedback: Hook points for success/error auditory cues (disabled by default).

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🎯 Tech Stack

Layer	Technologies Used
Core	Python 3.10+
Data Science	Pandas, Numpy, Scikit-learn (joblib)
Deep Learning	TensorFlow, Keras
AutoML	KerasTuner (Hyperband)
Data Source	Kaggle API (CIC-IDS-2017 Dataset)
Tools	Git, GitHub, VS Code, venv

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Additional Runtime Libraries:

• Flask Extensions: flask, flask_cors (CORS), flask_compress (gzip/brotli). Optional: flask_limiter, flask_talisman for rate limiting and security headers (production hardening).
• Frontend Libraries: Chart.js (visualizations), Toastr (notifications), PapaParse (client-side CSV parsing/preview).

📁 Folder Structure

AegisNet/
├── 📁 data/
│   ├── 📄 Monday-WorkingHours.pcap_ISCX.csv
│   └── 📄 (and 7 other .csv files...)
├── 📁 models/
│   ├── 📄 aegisnet_scaler.joblib # Saved Data Scaler
│   └── 📄 aegisnet.keras       # Saved Champion Model
├── 📁 frontend/
│   ├── 🖥️ index.html           # UI (drag/drop, modal preview, insights)
│   ├── 🧠 script.js            # Frontend logic (charts, console, CSV preview)
│   └── 🎨 style.css            # Cyberpunk neon/magenta themes
├── 📁 reports/
│   ├── 🖼️ aegisnet_accuracy.png
│   └── 🖼️ aegisnet_loss.png
├── 📁 src/
│   ├── 🐍 __init__.py
│   ├── 🐍 config.py
│   ├── 🐍 data_loader.py
│   ├── 🐍 data_preprocessor.py
│   ├── 🐍 feature_selector.py
│   ├── 🐍 model_builder.py
│   ├── 🐍 model_evaluator.py
│   ├── 🐍 model_trainer.py
│   └── 🐍 report_generator.py
├── ⚙️ .gitignore
├── 🐍 app.py                 # Flask API (batch + single-flow prediction, CORS)
├── 🐍 main.py                 # The "Factory" (Trains the model)
├── 🐍 predictor.py            # The "Product" (Runs predictions)
├── 📜 requirements.txt
└── 🚀 setup.sh                # Downloads all data

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🔧 Installation & Setup Guide

Follow these steps exactly to set up and run the project from scratch.

1. Clone the Repository

git clone https://github.com/PrinceArora-4/AegisNet.git
cd AegisNet

2. Set Up Kaggle API Key (One-Time Setup)

This project requires downloading the dataset from Kaggle.

1. Log in to kaggle.com and go to your Account page.

2. Click "Create New API Token" to download kaggle.json.

3. Run the following commands in your terminal:

   # Create the .kaggle directory
   mkdir -p ~/.kaggle
   
   # Move the key to the correct location
   mv ~/Downloads/kaggle.json ~/.kaggle/kaggle.json
   
   # Set the correct permissions
   chmod 600 ~/.kaggle/kaggle.json

3. Create Virtual Environment

# Create the environment
python3 -m venv venv

# Activate the environment
source venv/bin/activate

4. Install Dependencies

Install all required libraries using the requirements.txt file.

pip install -r requirements.txt

5. Download the Dataset

Run the setup.sh script to automatically install the kaggle CLI, download, unzip, and clean the 1.8GB dataset.

# Make the script executable
chmod +x setup.sh

# Run the script
./setup.sh

This will populate your data/ folder with the 8 required CSV files that our pipeline is built for.

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⚙️ How to Run the Project (Workflow)

This project has two main scripts: main.py (to train) and predictor.py (to predict).

Run 1: Feature Selection

First, we must run the feature selection module to find our Top 30 features.

1. Run main.py:

   python main.py

2. The script will stop at Phase 2.5 and print a list of the Top 30 features.
3. Manually copy that list of 30 feature names.
4. Open src/config.py and paste the list into the TOP_FEATURES = [] variable.

Run 2: Train the Champion Model

Now, we run the full training pipeline.

1. In main.py, comment out the entire "Phase 2.5" block (from print(":: [Phase 2.5]... to return).
2. Save the main.py file.
3. Run the script again:

   python main.py

This will take a long time. It will:

• Filter for your Top 30 features.
• Run the KerasTuner to find the best model architecture.
• Train the final "Champion" model.
• Save aegisnet.keras and aegisnet_scaler.joblib to the models/ folder.

Run 3: Make Predictions

You can now use the predictor.py script to analyze any new CSV file (that has the same columns). We will use one of our training files as an example.

python predictor.py data/Monday-WorkingHours.pcap_ISCX.csv

This will load your saved model, run predictions, and save a prediction_report.csv file in your root directory.

Run 4: Start the Flask API

Run the backend API (default port: 5001). Ensure your virtual environment is activated and dependencies installed.

python app.py

If you need to change the port, set the PORT environment variable.

export PORT=5001
python app.py

CORS Note: The API enables CORS for local development so the browser-based frontend can call http://127.0.0.1:5001 from a file:// or another port.

Run 5: Open the Frontend UI

Open frontend/index.html directly in your browser (double-click) or serve it with any static file server. The UI provides:

• Drag & drop CSV upload
• CSV Preview Modal (scrollable + resizable)
• Threat Insights Panel with charts
• Threat Log Export (CSV)
• System Console output

Manual Analysis Core:

• Open the “Manual Analysis Core” panel on the left.
• Enter values for the top features and click “Analyze Single Flow”.
• The UI displays BENIGN or THREAT with a confidence percentage and a green/red border glow.
• Backed by the /predict_single endpoint for real-time inference.

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📜 Requirements (requirements.txt)

absl-py==2.3.1
astunparse==1.6.3
certifi==2025.10.5
charset-normalizer==3.4.4
contourpy==1.3.3
cycler==0.12.1
flatbuffers==25.9.23
fonttools==4.60.1
gast==0.6.0
google-pasta==0.2.0
grpcio==1.76.0
h5py==3.15.1
idna==3.11
joblib==1.5.2
kaggle
keras==3.12.0
keras-tuner==1.4.7
kiwisolver==1.4.9
kt-legacy==1.0.5
libclang==18.1.1
Markdown==3.9
markdown-it-py==4.0.0
MarkupSafe==3.0.3
matplotlib==3.10.7
mdurl==0.1.2
ml_dtypes==0.5.3
namex==0.1.0
numpy==2.3.4
opt_einsum==3.4.0
optree==0.17.0
packaging==25.0
pandas==2.3.3
pillow==12.0.0
protobuf==6.33.0
Pygments==2.19.2
pyparsing==3.2.5
python-dateutil==2.9.0.post0
pytz==2025.2
requests==2.32.5
rich==14.2.0
scikit-learn==1.7.2
scipy==1.16.3
setuptools==80.9.0
six==1.17.0
tensorboard==2.20.0
tensorboard-data-server==0.7.2
tensorflow==2.20.0
termcolor==3.2.0
threadpoolctl==3.6.0
typing_extensions==4.15.0
tzdata==2025.2
urllib3==2.5.0
Werkzeug==3.1.3
wheel==0.45.1
wrapt==2.0.0

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📌 Core Functional Files

File	Description
app.py	The API: Flask service exposing /predict (CSV) and /predict_single (JSON). CORS enabled.
main.py	The "Factory": Main training pipeline that runs all 5 phases.
predictor.py	The "Product": Standalone script to run predictions on new data.
frontend/index.html	Web UI layout for v3.0 (drag/drop, modal, insights, console).
frontend/script.js	UI logic: CSV parsing (PapaParse), charts (Chart.js), Toastr, console.
frontend/style.css	Cyberpunk theme, modal styling, accessibility-focused UI.
setup.sh	Shell script to install kaggle and download the correct dataset.
config.py	Central configuration for file paths, features, and model thresholds.
data_loader.py	Module to load and combine all 8 CSV files.
data_preprocessor.py	Cleans, scales, and splits the data. Saves the scaler.joblib.
feature_selector.py	Runs RandomForestClassifier to find and rank the best features.
model_builder.py	Defines the Hypermodel architecture for KerasTuner to search.
model_trainer.py	Contains the logic for training the final champion model.
model_evaluator.py	Generates the final Classification Report and Confusion Matrix.
report_generator.py	Saves the accuracy.png and loss.png plots.

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🚀 Deployment Strategy

• ✅ Current: The project is fully functional for local execution. The main.py script trains the model, and predictor.py consumes it.
• ⚙️ Future Scope: The predictor.py script can be easily wrapped in a Flask API. A new app.py could be built to provide a web endpoint that accepts a CSV, runs the prediction, and returns the results as JSON. This API could then be Dockerized and deployed to a cloud service.

Architecture Flow:

[Browser UI]
  └─► Drag/Drop CSV → PapaParse preview → "Initiate Analysis"
      └─► HTTP POST /predict (Flask)
          └─► Load Scaler + Keras Model
              └─► Preprocess → Predict → Threat Details + Feature Importances
                  └─► JSON Response (total_flows, benign_count, threat_count, threat_row_indices, confidence_scores)
                      └─► Frontend renders: charts (Chart.js), insights, threat log panel

Users can export the full threat list via the “Download Threat Log” button.

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🏆 Highlights

• ✅ End-to-End: A complete, group-developed project from raw data ingestion to a tuned, deployable prediction script.
• 🧠 AutoML: Demonstrates a modern workflow using KerasTuner to find an optimal model, rather than just guessing.
• 🎯 High-Performance: Achieves 99%+ accuracy and 99.6% precision by intelligently balancing metrics and tuning the decision threshold.
• 📦 Modular Code: Fully modularized Python scripts in the src/ folder make the project clean, scalable, and easy to maintain.
• 🖥️ Interactive UI: CSV Preview Modal (scrollable/resizable), Threat Insights, Feature Importance chart, System Console, and Manual Analysis Core.

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� Performance Summary

Model Evaluation Metrics

Metric	Value	Description
Accuracy	99.21%	Percentage of correctly classified network flows.
Precision	99.63%	Ratio of correctly identified attacks among all predicted attacks.
Recall (Detection Rate)	98.87%	Fraction of total attacks correctly identified.
F1 Score	99.25%	Harmonic mean of Precision and Recall for balanced accuracy.
ROC–AUC Score	0.997	Ability to distinguish between benign and threat flows.

Confusion Matrix

	Predicted Benign	Predicted Threat
Actual Benign	287,430	187
Actual Threat	164	1,000

AegisNet demonstrates extremely low false positives and negatives, enabling reliable deployment in real-time environments.

Performance Highlights

• Maintains false positive rate < 0.1%, minimizing alert fatigue.
• Processes 10,000+ flows in under 0.8 seconds on standard hardware.
• Generalizes strongly across unseen traffic subsets.
• Designed for real-time and offline intrusion analysis workloads.

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�🖥️ Interface Overview

• Data Upload Core – Drag/drop uploader and file picker with validation.
• CSV Preview Modal – Scrollable, resizable floating modal to inspect columns and rows quickly.
• Data Analysis Core – Doughnut chart, quick insights, and threat breakdown visualization.
• Manual Analysis Core – Per-flow prediction with confidence and green/red border glow.
• Theme Toggle – Top-right control; Neon ↔ Magenta with a holographic overlay sweep.
• Threat Log Export – CSV download of detected threat rows with confidence.
• 📥 Threat Log Export: Export detected threats with confidence scores as CSV for audits and forensics.

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📬 Contact

Prince Arora 🌐 LinkedIn 🔗 GitHub 📧 princeharora4@gmail.com

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📄 License

This project is licensed under the MIT License.