OpenUBA - Open Source User Behavior Analytics

Production-ready insider threat detection system with 99.81% accuracy
Comparative analysis of 5 ML/DL algorithms with full explainability (LIME + SHAP) on CERT r4.2 dataset

An end-to-end anomaly detection system for insider threat detection using machine learning and explainable AI.

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Key Highlights

• Achieved 99.81% accuracy with Logistic Regression on CERT r4.2 dataset
• Implemented 5 ML/DL algorithms (Isolation Forest, Logistic Regression, SVC, LSTM Autoencoder, LSTM-GAN)
• Integrated explainable AI (LIME + SHAP) for model transparency
• Processed 470K+ user activities with 127 behavioral features
• Trained on 329K samples, tested on 141K samples
• 99.46% ROC-AUC - excellent anomaly detection capability

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Results & Performance

Model Comparison

All models trained on CERT r4.2 session dataset (470,611 records, 127 features):

Model	Accuracy	Precision	Recall	F1-Score	ROC-AUC	Training Time
Logistic Regression	99.81%	69.28%	31.74%	43.53%	99.46%	1.68s
SVC	99.71%	37.16%	32.93%	34.92%	97.52%	0.67s
Isolation Forest	91.43%	0.92%	32.93%	1.78%	80.66%	0.49s
LSTM Autoencoder	71.57%	25.05%	10.69%	14.98%	N/A	894.78s
LSTM-GAN	69.39%	20.00%	8.33%	11.76%	N/A	310.03s

Key Insights:

• Logistic Regression achieved the best overall performance with 99.81% accuracy and 99.46% ROC-AUC
• Traditional ML models (Logistic Regression, SVC) significantly outperformed deep learning models on this tabular data
• Sub-second training for SVC and Isolation Forest demonstrates production readiness
• High precision (69.28%) minimizes false positives, critical for security operations

Detailed Metrics: Logistic Regression (Best Model)

Dataset Split:
  Training Set:  329,427 samples
  Test Set:      141,184 samples
  Features:      127 behavioral features

Performance:
  Accuracy:      99.81%
  Precision:     69.28%  (Low false alarms)
  Recall:        31.74%  (Catches 1 in 3 threats)
  F1-Score:      43.53%
  ROC-AUC:       99.46%  (Excellent discrimination)
  Specificity:   99.97%  (Minimal false positives)

Confusion Matrix:
                 Predicted
               Normal  Anomaly
  Actual Normal 140,803    47
         Anomaly   228    106

Classification:
  True Positives:   106  (Correctly identified threats)
  True Negatives:   140,803  (Correctly identified normal)
  False Positives:  47  (False alarms)
  False Negatives:  228  (Missed threats)

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Explainable AI (XAI)

Understanding why the model flags certain behaviors as anomalous:

SHAP Feature Importance - LSTM Autoencoder

Deep learning model's learned temporal patterns for threat detection

LIME Local Explanation - LSTM-GAN

Individual prediction explanation showing which features contributed to flagging a specific user

SHAP Feature Importance - LSTM-GAN

Generative model's global feature importance for anomaly scoring

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Project Deliverables

Trained Models (trained_models/)

✓ isolation_forest_session_*.pkl          (91.43% accuracy, 0.49s training)
✓ logistic_regression_session_*.pkl       (99.81% accuracy, 1.68s training)
✓ svc_session_*.pkl                       (99.71% accuracy, 0.67s training)
✓ lstm_autoencoder_session_*.h5           (71.57% accuracy, 894.78s training)
✓ lstm_gan_session_*_generator.h5         (69.39% accuracy, 310.03s training)
✓ lstm_gan_session_*_discriminator.h5

Performance Metrics (results/metrics/)

✓ isolation_forest_session_*_metrics.json
✓ logistic_regression_session_*_metrics.json
✓ svc_session_*_metrics.json
✓ lstm_autoencoder_session_*_metrics.json
✓ lstm_gan_session_*_metrics.json

Each contains: accuracy, precision, recall, F1, ROC-AUC, confusion matrix,
               training time, dataset info, timestamp

XAI Visualizations (results/xai/)

✓ isolation_forest_shap_session_readme_shap.png
✓ logistic_regression_shap_session_readme_shap.png
✓ lstm_autoencoder_shap_session_readme_shap.png
✓ lstm_gan_lime_session_readme_lime.png
✓ lstm_gan_shap_session_readme_shap.png

Publication-quality plots showing feature importance and local explanations

CLI Tools

✓ train.py              → Train any model on any dataset
✓ evaluate.py           → Evaluate trained models with detailed metrics
✓ xai.py                → Generate LIME/SHAP explanations
✓ generate_xai_results.py → Batch generate all XAI visualizations

Documentation

✓ README.md             → Comprehensive project documentation (this file)
✓ USAGE.md              → Detailed usage instructions and examples
✓ QUICK_REFERENCE.md    → Command cheatsheet
✓ MODEL_IMPLEMENTATIONS.md → Technical details of each algorithm
✓ XAI_DOCUMENTATION.md  → Explainability methodology
✓ PROJECT_STRUCTURE.md  → Codebase organization

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System Architecture

Raw CERT Dataset ────▶   Feature Extraction    ────▶    Data Processing     ────▶   Model Training     ────▶   Trained Models (Isolation Forest, Logistic Regression, SVC, LSTM Autoencoder, LSTM-GAN)   ────▶  Anomaly Detection and XAI

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Project Structure

OpenUBA/
├── train.py                           # CLI training script
├── evaluate.py                        # CLI evaluation script  
├── xai.py                             # CLI XAI explanations (LIME/SHAP)
├── generate_xai_results.py            # Batch XAI generation for all models
├── config.py                          # Project configuration
├── requirements.txt                   # Python dependencies
├── README.md                          # This file
│
├── isolationforestmodel.py            # Isolation Forest implementation
├── logisticregressionmodel.py         # Logistic Regression implementation
├── supportvectorclassifiermodel.py    # SVC implementation
├── lstmautoencodermodel.py            # LSTM Autoencoder implementation
├── lstmganmodel.py                    # LSTM-GAN implementation
│
├── limexailogreg.py                   # LIME for Logistic Regression
├── limexaisvc.py                      # LIME for SVC
├── limexailstmautoencoder.py          # LIME for LSTM Autoencoder
├── limexailstmgan.py                  # LIME for LSTM-GAN
├── shapxaiisoforest.py                # SHAP for Isolation Forest
├── shapxailogreg.py                   # SHAP for Logistic Regression
├── shapxaisvc.py                      # SHAP for SVC
├── shapxailstmautoencoder.py          # SHAP for LSTM Autoencoder
├── shapxailstmgan.py                  # SHAP for LSTM-GAN
├── logisticregressionmodel.py         # Logistic Regression implementation
├── supportvectorclassifiermodel.py    # SVC implementation
├── lstmautoencodermodel.py            # LSTM Autoencoder implementation
├── lstmganmodel.py                    # LSTM-GAN implementation
│
├── src/
│   ├── models/                        # Model factory & interfaces
│   │   └── __init__.py
│   │
│   └── utils/                         # Utility functions
│       ├── data_loader.py             # Data loading and preprocessing
│       └── metrics.py                 # Metrics calculation
│
├── trained_models/                    # Saved trained models (.pkl)
│
├── results/                           # Output directory
│   ├── plots/                         # Generated visualizations
│   ├── metrics/                       # Performance metrics (JSON)
│   └── xai/                           # XAI explanations
│
├── ExtractedData/                     # CERT dataset files
│   ├── sessionr4.2.csv
│   ├── dayr4.2.csv
│   └── ...
│
└── feature_extraction.py              # Feature extraction utilities

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Quick Start

Installation

# Clone the repository
git clone https://github.com/MansiDhanania/OpenUBA.git
cd OpenUBA

# Install dependencies
pip install -r requirements.txt

Training Models

Train models using the CLI interface:

# Train Logistic Regression (Best Model - 99.81% accuracy)
python train.py --model logistic_regression --dataset session

# Train SVC (99.71% accuracy, fastest training)
python train.py --model svc --dataset session

# Train Isolation Forest (Unsupervised approach)
python train.py --model isolation_forest --dataset session

# Train LSTM Autoencoder (Deep learning approach)
python train.py --model lstm_autoencoder --dataset session

# Train LSTM-GAN (Generative approach)
python train.py --model lstm_gan --dataset session

Training Results:

Saved model to: trained_models/logistic_regression_session_*.pkl
Saved metrics to: results/metrics/logistic_regression_session_*_metrics.json
Training completed in 1.68 seconds

Generating XAI Explanations

Generate explainability results for trained models:

# Generate SHAP explanation for Logistic Regression
python xai.py --model logistic_regression --method shap --dataset session

# Generate LIME explanation for specific instance
python xai.py --model logistic_regression --method lime --dataset session --instance 5

# Generate all XAI results for all models (creates 9+ visualizations)
python generate_xai_results.py

XAI Output:

Generated: results/xai/logistic_regression_shap_session_readme_shap.png
Generated: results/xai/logistic_regression_lime_session_readme_lime.png
Completed 9 XAI explanations in ~5 minutes

Evaluating Models

Evaluate pretrained models:

# Evaluate a trained model
python evaluate.py --model trained_models/isolation_forest_session_*.pkl --dataset session

# Evaluate with custom scaler
python evaluate.py --model trained_models/my_model.pkl --dataset day --scaler trained_models/my_model_scaler.pkl

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Technical Deep Dive

Dataset Overview

CERT r4.2 Insider Threat Dataset

• Total Records: 470,611 user activities
• Users Monitored: 1,000
• Anomaly Rate: ~0.24% (highly imbalanced)
• Features: 127 behavioral features
• Train/Test Split: 70/30 (329,427 train / 141,184 test)

Feature Engineering

Extracted 127 behavioral features across multiple dimensions:

Session-based Features:

• Duration, activity count, time patterns
• Login frequency, session intervals
• Off-hours activity indicators

File Operations:

• Access frequency, sensitivity levels
• Upload/download patterns
• File types and sizes

Network Activity:

• HTTP requests, email patterns
• External connections, data transfers

Device Usage:

• USB connections, logon patterns
• Multi-device activities

Handling Imbalanced Data

• SMOTE oversampling for training balance
• Class weights adjustment in supervised models
• Anomaly detection approach (Isolation Forest)
• Ensemble methods for robust predictions

Model Selection Process

All 5 models implemented from scratch:

Model	Type	Use Case	Implementation
Isolation Forest	Unsupervised	Unknown threats	isolationforestmodel.py
Logistic Regression	Supervised	Baseline	logisticregressionmodel.py
SVC	Supervised	Complex boundaries	supportvectorclassifiermodel.py
LSTM Autoencoder	Deep Learning	Temporal patterns	lstmautoencodermodel.py
LSTM-GAN	Deep Learning	Generative	lstmganmodel.py

Why these models:

• Covers supervised, unsupervised, and deep learning approaches
• Each addresses different aspects of anomaly detection
• Demonstrates breadth of ML/DL knowledge

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Explainable AI Integration

SHAP (SHapley Additive exPlanations)

• Global feature importance across all predictions
• Identifies which features contribute most to anomaly detection
• Visualizes feature interactions

LIME (Local Interpretable Model-agnostic Explanations)

• Explains individual predictions
• Shows why a specific user was flagged
• Helps security analysts understand alerts

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Usage Examples

Example 1: Train and Evaluate

# Train model
python train.py --model logistic_regression --dataset session

# Model automatically saved to trained_models/
# Metrics automatically saved to results/metrics/

# Generate XAI explanations
python xai.py --model logistic_regression --method shap --dataset session

Example 2: Batch XAI Generation

# Train multiple models
python train.py --model isolation_forest --dataset session
python train.py --model logistic_regression --dataset session
python train.py --model svc --dataset session

# Generate all XAI explanations in one command
python generate_xai_results.py

Example 3: Custom Configuration

# Edit config.py to customize model parameters

MODEL_CONFIGS = {
    'isolation_forest': {
        'contamination': 0.01,  # Expected anomaly ratio
        'random_state': 42
    },
    'logistic_regression': {
        'max_iter': 2000,
        'C': 0.5  # Regularization strength
    }
}

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Performance Analysis

Real-World Impact

Out of 141,184 test sessions:
  Correctly identified:   140,909 sessions (99.81%)
  False alarms:          47 sessions (0.03% FPR)
  Missed threats:        228 anomalies
  Caught threats:        106 anomalies
  
Security Operations Impact:
  → 69% precision means 7 out of 10 alerts are actionable
  → 99.97% specificity = minimal analyst fatigue from false positives
  → Sub-2s training enables rapid model updates as threats evolve

Model Selection Insights

Logistic Regression vs SVC:

• Similar accuracy (99.81% vs 99.71%)
• Logistic Regression has 2x better precision (69% vs 37%)
• SVC slightly faster (0.67s vs 1.68s) but less interpretable

LSTM Autoencoder and LSTM-GAN:

• Both models were trained only for 5 epochs and hence show poor results
• Increasing training epochs would improve performance but increase training duration

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