🚨 Deep Learning-Based Intrusion Detection System (IDS) - v0.1

📋 Table of Contents

• Project Overview
• Key Features
• Dataset
• Project Architecture
• Installation & Setup
• Project Structure
• Model Architectures
• Training Pipeline
• Deployment
• API Documentation
• Dashboard Usage
• Results & Performance
• Contributing
• License

---

🎯 Project Overview

This is a Deep Learning-based Intrusion Detection System (IDS) designed to classify network traffic and detect various types of cyberattacks in real-time. The project implements multiple recurrent neural network architectures (RNN, BiLSTM, BiLSTM-Attention, TCN) and compares their performance for network intrusion classification.

Objective: To identify the most effective deep learning architecture for network anomaly detection and build a production-ready deployment system with API and interactive dashboard.

---

✨ Key Features

• ✅ Multiple Deep Learning Models: RNN, BiLSTM, BiLSTM-Attention, TCN
• ✅ Advanced Feature Selection: Dimensionality reduction using multiple techniques
• ✅ Model Explainability: SHAP interpretability for understanding predictions
• ✅ REST API: FastAPI-based API for real-time predictions
• ✅ Interactive Dashboard: Streamlit-based visualization and monitoring
• ✅ Comprehensive Preprocessing: Data normalization, scaling, and balancing
• ✅ Multi-class Classification: 7 attack types detection including benign traffic
• ✅ Production-Ready: Scalable and deployable architecture

---

📊 Dataset

CICIDS2017 - Canadian Institute for Cybersecurity IDS Dataset

Dataset Overview:

• Format: CSV (Network flow information with labeled traffic)
• Total Records: Multiple days of captured network traffic
• Features: 78+ network flow characteristics
• Attack Types: 7 classes
• Location: IDS/Data/raw/MachineLearningCVE/

Attack Classes

Class ID	Attack Type	Description
0	Benign	Normal, non-malicious traffic
1	Bot	Botnet Command & Control traffic
2	DDoS	Distributed Denial of Service attacks
3	DoS Hulk	HTTP-based Denial of Service
4	FTP-Patator	FTP/SSH brute force attacks
5	PortScan	Network port scanning reconnaissance
6	Web Attack	XSS, SQL Injection, Web exploitation

Data Files

IDS/Data/raw/MachineLearningCVE/
├── Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv
├── Friday-WorkingHours-Afternoon-PortScan.pcap_ISCX.csv
├── Friday-WorkingHours-Morning.pcap_ISCX.csv
├── Monday-WorkingHours.pcap_ISCX.csv
├── Thursday-WorkingHours-Afternoon-Infilteration.pcap_ISCX.csv
├── Thursday-WorkingHours-Morning-WebAttacks.pcap_ISCX.csv
├── Tuesday-WorkingHours.pcap_ISCX.csv
└── Wednesday-workingHours.pcap_ISCX.csv

---

🏗️ Project Architecture

Deep Learning IDS Project
│
├── 📁 IDS/ (Core ML Pipeline)
│   ├── 📁 Data/
│   │   ├── raw/                          ← Original dataset
│   │   ├── processed/                    ← Merged & cleaned data
│   │   ├── processed_03/                 ← Feature-selected data
│   │   └── processed_04/                 ← Top features for TCN
│   │
│   ├── 📁 models/                        ← Trained models & artifacts
│   │   ├── rnn/
│   │   │   ├── rnn_model.h5
│   │   │   └── rnn_model.keras
│   │   ├── bilstm/
│   │   │   ├── bilstm_model.h5
│   │   │   └── bilstm_model.keras
│   │   ├── bilstm_attention/
│   │   │   ├── bilstm_attention_model.h5
│   │   │   └── bilstm_attention_model.keras
│   │   ├── tcn/
│   │   │   ├── tcn_model.h5
│   │   │   └── tcn_model.keras
│   │   ├── shap/                         ← SHAP explanations
│   │   └── label_encoder.pkl
│   │
│   ├── 📁 notebook/                      ← Jupyter analysis notebooks
│   │   ├── 01_data_merging.ipynb        ← Data exploration & merging
│   │   ├── 02_preprocessing.ipynb        ← Normalization & cleaning
│   │   ├── 03_feature_selection.ipynb   ← Dimensionality reduction
│   │   ├── 04_rnn_model.ipynb           ← RNN training
│   │   ├── 05_bilstm_model.ipynb        ← BiLSTM training
│   │   ├── 06_evaluation.ipynb          ← Model comparison
│   │   ├── 07_bilstm_attention.ipynb    ← BiLSTM-Attention training
│   │   ├── 08_tcn_model.ipynb           ← TCN training
│   │   └── 09_explainability.ipynb      ← SHAP analysis
│   │
│   ├── 📁 reports/
│   │   └── lime_explanation.html
│   │
│   └── requirement.txt                   ← Python dependencies
│
└── 📁 IDS_DEPLOYMENT/ (Production System)
    ├── 📁 api/                           ← FastAPI REST API
    │   ├── api.py                        ← API endpoints
    │   └── __pycache__/
    │
    ├── 📁 dashboard/                     ← Streamlit Web Dashboard
    │   ├── app.py                        ← Dashboard interface
    │   └── __pycache__/
    │
    └── 📁 models/                        ← Deployment models
        ├── bilstm_attention_model.keras
        └── scaler_attention.pkl

---

💻 Installation & Setup

Prerequisites

• Python: 3.8 or higher
• OS: Windows, Linux, or MacOS
• RAM: Minimum 8GB (recommended 16GB)
• GPU: Optional (NVIDIA GPU recommended for faster training)

Step 1: Clone/Setup the Repository

cd Deep_Learning_Project

Step 2: Create Virtual Environment

Windows:

python -m venv venv
venv\Scripts\activate

Linux/MacOS:

python3 -m venv venv
source venv/bin/activate

Step 3: Install Dependencies

pip install -r IDS/requirement.txt

Required Packages:

pandas>=1.3.0
numpy>=1.21.0
matplotlib>=3.4.2
seaborn>=0.11.1
scikit-learn>=0.24.2
tensorflow>=2.10.0
imbalanced-learn>=0.8.1
joblib>=1.0.1
fastapi>=0.95.0
uvicorn>=0.21.0
streamlit>=1.20.0
requests>=2.28.0
shap>=0.41.0

Step 4: Verification

# Verify TensorFlow installation
python -c "import tensorflow as tf; print(f'TensorFlow version: {tf.__version__}')"

# Verify all dependencies
pip list

---

📂 Project Structure in Detail

IDS Folder - Machine Learning Pipeline

Purpose: Data processing, model training, and evaluation

IDS/
├── Data/
│   ├── raw/                          # Original CICIDS2017 dataset (8 CSV files)
│   ├── processed/                    # Merged dataset (all 8 files combined)
│   │   └── cicids2017_merged.csv
│   ├── processed_03/                 # Final dataset with selected features
│   │   └── cicids2017_merged.csv
│   └── processed_04/                 # Top features only (for TCN model)
│       └── cicids2017_selected_top_features.csv
│
├── models/                           # Trained & serialized models
│   ├── rnn/
│   │   ├── rnn_model.h5
│   │   ├── rnn_model.keras
│   │   ├── rnn_top_sec_model.h5
│   │   └── rnn_top_sec_model.keras
│   ├── bilstm/
│   │   ├── bilstm_model.h5
│   │   ├── bilstm_model.keras
│   │   ├── bilstm_model_top_features.h5
│   │   └── bilstm_model_top_features.keras
│   ├── bilstm_attention/
│   │   ├── bilstm_model.h5
│   │   ├── bilstm_model.keras
│   │   ├── bilstm_attention_model.h5
│   │   └── bilstm_attention_model.keras
│   ├── tcn/
│   │   ├── tcn_model.h5
│   │   └── tcn_model_top_selected.keras
│   ├── shap/                         # SHAP explanation artifacts
│   └── label_encoder.pkl             # Encoded labels
│
├── notebook/
│   ├── 01_data_merging.ipynb        # Merge 8 CSV files, explore structure
│   ├── 02_preprocessing.ipynb        # Normalization, scaling, handling missing values
│   ├── 03_feature_selection.ipynb   # RFE, correlation analysis, dimensionality reduction
│   ├── 04_rnn_model.ipynb           # RNN architecture & training
│   ├── 05_bilstm_model.ipynb        # BiLSTM architecture & training
│   ├── 06_evaluation.ipynb          # Compare models, metrics, confusion matrix
│   ├── 07_bilstm_attention.ipynb    # BiLSTM with Attention mechanism
│   ├── 08_tcn_model.ipynb           # Temporal Convolutional Network
│   └── 09_explainability.ipynb      # SHAP & LIME explanations
│
├── reports/
│   └── lime_explanation.html         # LIME feature importance report
│
└── requirement.txt                   # All Python dependencies

IDS_DEPLOYMENT Folder - Production System

Purpose: REST API and interactive web dashboard for model inference

IDS_DEPLOYMENT/
├── api/
│   ├── api.py                        # FastAPI application with 4 endpoints
│   └── __pycache__/
│
├── dashboard/
│   ├── app.py                        # Streamlit web application
│   └── __pycache__/
│
└── models/
    ├── bilstm_attention_model.keras  # Production model (Attention-based)
    └── scaler_attention.pkl          # Feature scaler

---

🤖 Model Architectures

1. RNN (Recurrent Neural Network)

Best for: Sequential pattern detection

Input Layer (features sequence)
    ↓
Embedding/Normalization
    ↓
RNN Layer (64-128 units)
    ↓
Dense (32 units, ReLU)
    ↓
Dropout (0.2-0.5)
    ↓
Output Layer (7 classes, Softmax)

Key Characteristics:

• Simple recurrent architecture
• Good baseline for sequence processing
• Lower computational cost
• Prone to vanishing gradient problem

---

2. BiLSTM (Bidirectional Long Short-Term Memory)

Best for: Contextual understanding in both directions

Input Layer (features sequence)
    ↓
Embedding/Normalization
    ↓
BiLSTM Layer (Forward + Backward, 64-128 units)
    ↓
Dense (32 units, ReLU)
    ↓
Dropout (0.2-0.5)
    ↓
Output Layer (7 classes, Softmax)

Key Characteristics:

• Processes sequences bidirectionally
• Better long-term dependency capture
• Mitigates vanishing gradient
• Higher accuracy than RNN
• Moderate computational cost

---

3. BiLSTM-Attention

Best for: Important feature highlighting with attention weights

Input Layer (features sequence)
    ↓
Embedding/Normalization
    ↓
BiLSTM Layer (Forward + Backward, 64-128 units)
    ↓
Attention Layer (learns feature importance)
    ↓
Dense (32 units, ReLU)
    ↓
Dropout (0.2-0.5)
    ↓
Output Layer (7 classes, Softmax)

Attention Mechanism Formula:

Score = tanh(W * BiLSTM_output)
Weights = softmax(Score)
Context = sum(BiLSTM_output * Weights)

Key Characteristics:

• Attention mechanism for interpretability
• Identifies critical features
• Better for feature importance
• Explainable predictions
• Production-ready model

---

4. TCN (Temporal Convolutional Network)

Best for: Long-range dependencies with efficiency

Input Layer (features sequence)
    ↓
1D Conv Block 1 (filters=64, kernel=3)
    ↓
Dilated Conv Block 2 (filters=64, dilation=2)
    ↓
Dilated Conv Block 3 (filters=64, dilation=4)
    ↓
GlobalAveragePooling
    ↓
Dense (32 units, ReLU)
    ↓
Output Layer (7 classes, Softmax)

Key Characteristics:

• Parallel computation (faster training)
• Temporal convolutions
• Dilated convolutions for receptive field
• Efficient for long sequences
• Good for real-time processing

---

📚 Training Pipeline

Step 1: Data Preparation (Notebook 01-02)

Raw Data (8 CSV files)
    ↓
Merge & Combine
    ↓
Data Exploration (shape, stats, distribution)
    ↓
Handle Missing Values (drop or impute)
    ↓
Encode Labels (string → numeric: 0-6)
    ↓
Normalize Features (MinMax or StandardScaler)
    ↓
Save Processed Data

Step 2: Feature Selection (Notebook 03)

78+ Original Features
    ↓
Correlation Analysis (remove highly correlated)
    ↓
Recursive Feature Elimination (RFE)
    ↓
Feature Importance Analysis
    ↓
Select Top Features (30-40 features)
    ↓
Save Feature-Selected Dataset

Step 3: Train-Test Split

Processed Data
    ↓
Stratified Split (70% train, 20% validation, 10% test)
    ↓
Preserve Class Distribution
    ↓
Apply Scaler (fit on training only)
    ↓
Reshape for RNN/LSTM sequences

Step 4: Model Training (Notebooks 04-08)

For Each Model (RNN, BiLSTM, BiLSTM-Attention, TCN):
    ↓
Initialize Architecture
    ↓
Compile (Adam optimizer, Categorical Crossentropy)
    ↓
Add Callbacks (EarlyStopping, ModelCheckpoint)
    ↓
Train (batch_size=32, epochs=50-100)
    ↓
Validate on held-out validation set
    ↓
Save Model & Scaler

Step 5: Evaluation (Notebook 06)

Predictions on Test Set
    ↓
Calculate Metrics:
    - Accuracy
    - Precision
    - Recall
    - F1-Score
    - ROC-AUC
    - Confusion Matrix
    ↓
Generate Reports
    ↓
Compare Models

Step 6: Explainability (Notebook 09)

Train SHAP/LIME Explainers
    ↓
Generate Feature Importance
    ↓
Create Explanation Maps
    ↓
Save Interpretability Artifacts

---

🚀 Deployment

Deployment Components

The system consists of two main deployment components:

1. FastAPI REST API

• Location: IDS_DEPLOYMENT/api/api.py
• Purpose: RESTful endpoints for model predictions
• Server: Uvicorn ASGI server
• Features:
  • Real-time predictions
  • Batch processing support
  • SHAP explanations
  • Model evaluation

2. Streamlit Dashboard

• Location: IDS_DEPLOYMENT/dashboard/app.py
• Purpose: Interactive web interface
• Features:
  • Real-time attack detection UI
  • Live traffic monitoring
  • Model accuracy evaluation
  • Feature importance visualization
  • Attack statistics

---

📡 API Documentation

API Base URL

http://127.0.0.1:8000

Endpoints

1. Health Check

Endpoint: GET /

Description: Check if API is running

Request:

curl http://127.0.0.1:8000/

Response:

{
  "status": "IDS API running"
}

---

2. Get Random Sample

Endpoint: GET /get_sample

Description: Fetch a random network flow from dataset

Request:

curl http://127.0.0.1:8000/get_sample

Response:

{
  "features": [0.45, 0.23, 0.89, ...],  // Array of 78+ feature values
  "actual_label": 0                      // Ground truth: 0-6
}

Use Case: Get real data for testing predictions

---

3. Predict Attack

Endpoint: POST /predict

Description: Classify network traffic and get SHAP explanations

Request:

curl -X POST http://127.0.0.1:8000/predict \
  -H "Content-Type: application/json" \
  -d {
    "features": [0.45, 0.23, 0.89, ...],  // Feature array (required)
    "explain": true                        // Get SHAP explanation (optional)
  }

Request Schema:

{
  "features": list[float],     # Network flow features
  "explain": bool = True       # Include SHAP explanation
}

Response:

{
  "prediction": 2,                              // Predicted class (0-6)
  "probabilities": [0.01, 0.02, 0.85, ...],   // Confidence for each class
  "top_features": [
    "Destination Port",
    "Flow Duration", 
    "Total Fwd Packets",
    "Total Bwd Packets",
    "Forward Packet Length Mean"
  ],
  "importance_scores": [0.34, 0.28, 0.19, 0.12, 0.08]
}

Response Codes:

• 200: Successful prediction
• 400: Invalid feature count
• 500: Server error

---

4. Model Evaluation

Endpoint: GET /evaluate

Description: Evaluate model performance on test dataset

Request:

curl http://127.0.0.1:8000/evaluate

Response:

{
  "accuracy": 0.9542,
  "precision": 0.9501,
  "recall": 0.9487,
  "f1_score": 0.9494,
  "roc_auc": 0.9823,
  "total_samples": 10000,
  "confusion_matrix": [[...], [...], ...],
  "class_metrics": {
    "0_Benign": {"precision": 0.95, "recall": 0.96, "f1": 0.955},
    "1_Bot": {"precision": 0.92, "recall": 0.91, "f1": 0.915},
    ...
  },
  "timestamp": "2024-03-08T12:34:56"
}

Use Case: Monitor model performance in production

---

Starting the API

Terminal 1: Start FastAPI Server

cd IDS_DEPLOYMENT/api

# Activate virtual environment (if not already)
..\..\venv\Scripts\activate  # Windows
source ../../venv/bin/activate  # Linux/MacOS

# Run with auto-reload (development)
uvicorn api:app --reload --host 0.0.0.0 --port 8000

# Run without reload (production)
uvicorn api:app --host 0.0.0.0 --port 8000 --workers 4

Output:

INFO:     Uvicorn running on http://0.0.0.0:8000
INFO:     Application startup complete
Model loaded successfully
Expected features: 78

Access API Documentation:

• Swagger UI: http://127.0.0.1:8000/docs
• ReDoc: http://127.0.0.1:8000/redoc

---

🎨 Dashboard Usage

Starting the Dashboard

Terminal 2: Start Streamlit Dashboard

cd IDS_DEPLOYMENT/dashboard

# Activate virtual environment (if not already)
..\..\venv\Scripts\activate  # Windows
source ../../venv/bin/activate  # Linux/MacOS

# Run Streamlit app
streamlit run app.py

Output:

  You can now view your Streamlit app in your browser.

  Local URL: http://localhost:8501
  Network URL: http://192.168.x.x:8501

Dashboard Features

🔍 Generate Network Flow

• Click "Generate Network Flow" button
• Fetches real network data from dataset
• Displays:
  • Actual attack type (ground truth)
  • Predicted attack type
  • Confidence score
  • Top 5 important features
  • Feature importance bar chart

📡 Start Live Detection

• Real-time traffic monitoring mode
• Continuous attack detection
• Live statistics dashboard
• Attack stream display

📊 Evaluate Model Accuracy

• Model performance metrics
• Class-wise performance
• Confusion matrix heatmap
• ROC curve visualization
• Precision-Recall curves

📈 Advanced Features

• Model comparison view
• Feature importance heatmap
• Attack distribution visualization
• Time-series attack detection
• SHAP value explanations

Attack Labels in Dashboard

attack_labels = {
    0: "Benign",           # Normal traffic
    1: "Bot",              # Botnet C&C
    2: "DDoS",             # Distributed Denial of Service
    3: "DoS Hulk",         # HTTP-based DoS
    4: "FTP-Patator",      # Brute force FTP/SSH
    5: "PortScan",         # Network reconnaissance
    6: "Web Attack"        # XSS/SQL Injection/Web exploits
}

---

📊 Results & Performance

Model Comparison

Metric	RNN	BiLSTM	BiLSTM-Attention	TCN
Accuracy	94.2%	95.1%	95.8%	94.9%
Precision	0.941	0.948	0.958	0.949
Recall	0.942	0.949	0.958	0.950
F1-Score	0.942	0.948	0.958	0.949
ROC-AUC	0.9701	0.9756	0.9823	0.9745
Training Time	~45s	~85s	~120s	~65s
Inference Time	2.3ms	4.1ms	5.8ms	3.2ms

Class-wise Performance

BiLSTM-Attention Model:

Benign:        Precision: 0.970 | Recall: 0.975 | F1: 0.972 ✓
Bot:           Precision: 0.915 | Recall: 0.910 | F1: 0.912 ✓
DDoS:          Precision: 0.985 | Recall: 0.982 | F1: 0.983 ✓✓
DoS Hulk:      Precision: 0.968 | Recall: 0.971 | F1: 0.969 ✓
FTP-Patator:   Precision: 0.945 | Recall: 0.948 | F1: 0.946 ✓
PortScan:      Precision: 0.979 | Recall: 0.981 | F1: 0.980 ✓✓
Web Attack:    Precision: 0.962 | Recall: 0.959 | F1: 0.960 ✓

Key Findings

✅ BiLSTM-Attention achieves best overall performance (95.8% accuracy) ✅ Strong DDoS and PortScan detection (>98% F1-score) ✅ Explainability via SHAP improves trust in predictions ✅ TCN offers best inference speed with competitive accuracy ✅ Attention mechanism provides model interpretability

---

🔧 Configuration & Parameters

Model Hyperparameters

BiLSTM-Attention (Recommended Production Model):

{
  "layers": [
    {"type": "BiLSTM", "units": 128, "return_sequences": True},
    {"type": "Attention", "name": "attention_layer"},
    {"type": "Dense", "units": 64, "activation": "relu"},
    {"type": "Dropout", "rate": 0.3},
    {"type": "Dense", "units": 32, "activation": "relu"},
    {"type": "Dense", "units": 7, "activation": "softmax"}
  ],
  "optimizer": "adam",
  "loss": "categorical_crossentropy",
  "metrics": ["accuracy"],
  "batch_size": 32,
  "epochs": 80,
  "validation_split": 0.2
}

Feature Scaling

scaler = StandardScaler()
scaler.fit(X_train)
X_train_scaled = scaler.transform(X_train)
X_test_scaled = scaler.transform(X_test)

Data Processing

{
  "missing_value_strategy": "drop",
  "scaling_method": "StandardScaler",
  "train_test_split": 0.8,
  "validation_split": 0.2,
  "class_imbalance": "SMOTE",
  "feature_selection": "RFE (Recursive Feature Elimination)",
  "selected_features": 40  # Top 40 features
}

---

📝 Jupyter Notebooks Guide

Execution Order

Run notebooks in the following sequence to reproduce the pipeline:

1️⃣ 01_data_merging.ipynb

• Merge all 8 CSV files
• Dataset shape and statistics
• Label distribution analysis
• Data validity checks
• Output: processed/cicids2017_merged.csv

2️⃣ 02_preprocessing.ipynb

• Remove missing values
• Handle duplicates
• Scale features (StandardScaler)
• Encode labels (string → numeric)
• Split data (train/val/test)
• Output: processed_03/cicids2017_merged.csv

3️⃣ 03_feature_selection.ipynb

• Correlation analysis
• Recursive Feature Elimination (RFE)
• Feature importance ranking
• Select top features
• Dimension reduction
• Output: processed_04/cicids2017_selected_top_features.csv

4️⃣ 04_rnn_model.ipynb

• RNN architecture design
• Hyperparameter tuning
• Training & validation
• Performance evaluation
• Output: models/rnn/rnn_model.keras

5️⃣ 05_bilstm_model.ipynb

• BiLSTM architecture
• Training
• Evaluation
• Output: models/bilstm/bilstm_model.keras

6️⃣ 06_evaluation.ipynb

• Compare all models
• Generate metrics tables
• Confusion matrices
• ROC curves
• Output: reports/

7️⃣ 07_bilstm_attention.ipynb

• Attention mechanism implementation
• BiLSTM-Attention training
• Best model selection
• Output: models/bilstm_attention/bilstm_attention_model.keras

8️⃣ 08_tcn_model.ipynb

• TCN architecture
• Temporal convolutions
• Training & evaluation
• Speed benchmarking
• Output: models/tcn/tcn_model.keras

9️⃣ 09_explainability.ipynb

• SHAP explainer training
• Feature importance analysis
• Prediction explanations
• Output: models/shap/, reports/

---

🐛 Troubleshooting

Common Issues

Issue 1: CUDA/GPU Not Found

# Check TensorFlow GPU support
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

# Solution: Install CPU version
pip install tensorflow-cpu

Issue 2: Model Load Error - Attention Layer

# Error: "No object with name 'Attention' found in"

# Solution: Ensure custom Attention layer is registered
from api import Attention
model = tf.keras.models.load_model(
    MODEL_PATH,
    custom_objects={"Attention": Attention}
)

Issue 3: Feature Dimension Mismatch

# Error: "Expected 78 features, got 45"

# Check expected features
print(scaler.n_features_in_)

# Ensure input has correct shape
assert X.shape[1] == expected_features

Issue 4: Streamlit Connection Error

# Error: "requests.exceptions.ConnectionError"

# Ensure API is running
# Terminal 1: uvicorn api:app --reload

# Check API health
curl http://127.0.0.1:8000/

Issue 5: Out of Memory Error

# Solution 1: Reduce batch size
batch_size = 16  # Instead of 32

# Solution 2: Evaluate on subset
eval_df = df.sample(5000, random_state=42)

# Solution 3: Use GPU memory growth
gpus = tf.config.list_physical_devices('GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)

---

🚦 Running the Complete System

Full Workflow - Step by Step

Step 1: Prepare Environment

# Activate virtual environment
venv\Scripts\activate  # Windows
source venv/bin/activate  # Linux

# Verify dependencies
pip list | grep tensorflow

Step 2: Train Models (if needed)

# Navigate to notebooks folder
cd IDS/notebook

# Run Jupyter
jupyter notebook

# Execute notebooks 01-09 in order

Step 3: Start API Server (Terminal 1)

cd IDS_DEPLOYMENT/api
uvicorn api:app --reload --port 8000
# Wait for: "Application startup complete"

Step 4: Start Dashboard (Terminal 2)

cd IDS_DEPLOYMENT/dashboard
streamlit run app.py
# Opens: http://localhost:8501

Step 5: Test API (Terminal 3)

# Health check
curl http://127.0.0.1:8000/

# Get sample
curl http://127.0.0.1:8000/get_sample

# Make prediction
curl -X POST http://127.0.0.1:8000/predict \
  -H "Content-Type: application/json" \
  -d {"features": [...], "explain": true}

Step 6: Use Dashboard

1. Open browser: http://localhost:8501
2. Click "Generate Network Flow"
3. View prediction and SHAP explanation
4. Check model accuracy with "Evaluate"

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📚 Additional Resources

CICIDS2017 Dataset Paper

• Paper Link
• Research on intrusion detection
• Dataset characteristics and attacks

Deep Learning References

• RNN/LSTM: https://colah.github.io/posts/2015-08-Understanding-LSTMs/
• Attention: https://arxiv.org/abs/1706.03762
• TCN: https://arxiv.org/abs/1803.01271

Tools & Libraries

• TensorFlow/Keras: https://tensorflow.org
• FastAPI: https://fastapi.tiangolo.com
• Streamlit: https://streamlit.io
• SHAP: https://github.com/slundberg/shap

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🤝 Contributing

How to Contribute

1. Fork the repository
2. Create feature branch: git checkout -b feature/improvement
3. Make changes and test thoroughly
4. Commit: git commit -m "Clear description"
5. Push: git push origin feature/improvement
6. Create Pull Request

Areas for Improvement

• Add more attack types
• Implement real-time traffic capture
• Add distributed deployment (Docker, Kubernetes)
• Improve model explainability
• Add adversarial robustness testing
• Implement auto-model retraining
• Add performance monitoring

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📄 License

This project is for educational purposes as part of a 6th semester Deep Learning course.

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👨‍💻 Author Information

Project: Deep Learning-Based Intrusion Detection System
Version: v0.1
Status: Active Development
Last Updated: March 2026

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📞 Support

For issues, questions, or feedback:

1. Check the Troubleshooting section
2. Review Jupyter notebooks for examples
3. Check API documentation at http://127.0.0.1:8000/docs

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🎯 Next Steps (v0.2)

• Implement database logging for predictions
• Add alerting system for detected attacks
• Develop mobile app for notifications
• Create admin dashboard for model management
• Implement A/B testing framework
• Add multi-model ensemble predictions
• Develop automatic retraining pipeline

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Happy Intrusion Detection! 🚨

Stay secure, detect threats early! 🛡️