Multi-User Authentication System using Neural Networks

📋 Overview

This project implements a comprehensive multi-user authentication system using neural networks for analyzing accelerometer-based behavioral biometrics. The system processes acceleration data from 10 different users and employs both pre-optimized and post-optimized neural network architectures to achieve robust user authentication.

🎯 Project Objectives

• Develop a robust multi-user authentication system using accelerometer data
• Compare performance between pre-optimized and post-optimized neural network architectures
• Analyze behavioral biometric patterns across different data domains (time, frequency, and combined)
• Implement comprehensive performance evaluation metrics for authentication systems

📁 Project Structure

PUSL3123-Coursework/
├── README.md                           # This file
├── PUSL3123-Coursework-2024.pdf       # Project specification document
├── (01)-Data-Analysis/                 # Initial data analysis scripts
│   ├── load_data.m                     # Data loading utility
│   ├── analyze_inter_variance.m        # Inter-user variance analysis
│   └── analyze_intra_variance.m        # Intra-user variance analysis
├── (02)-Pre-optimized-Neural-Network/  # Pre-optimization implementation
│   ├── main.m                          # Main execution script
│   ├── 1_data_analysis/               # Data analysis modules
│   ├── 2_neural_network/              # Neural network implementation
│   │   ├── prepare_data/              # Data preparation utilities
│   │   ├── train/                     # Training modules
│   │   ├── evaluate/                  # Evaluation utilities
│   │   └── results/                   # Output results
│   └── CW-Data/                       # Dataset (60 .mat files)
└── (03)-Post-optimized-Neural-Network/ # Post-optimization implementation
    ├── main.m                          # Main execution script
    ├── 1_data_analysis/               # Enhanced data analysis
    ├── 2_neural_network/              # Optimized neural network
    │   ├── prepare_data/              # Enhanced data preparation
    │   ├── train/                     # Optimized training algorithms
    │   ├── evaluate/                  # Advanced evaluation metrics
    │   └── results/                   # Comparative results
    └── CW-Data/                       # Dataset (60 .mat files)

🔬 Dataset Description

The dataset consists of accelerometer-based behavioral biometric data from 10 users (U01-U10). Each user has 6 different data files representing different feature extraction methods and collection scenarios:

Data Types:

• Time Domain (TimeD): Raw accelerometer signals in time domain
• Frequency Domain (FreqD): Frequency-transformed features
• Combined (TimeD_FreqD): Hybrid time-frequency features

Collection Scenarios:

• Same Day (FDay): Training and testing data collected on the same day
• Cross Day (MDay): Training and testing data collected on different days

File Naming Convention:

U[XX]_Acc_[Domain]_[Scenario].mat

• XX: User ID (01-10)
• Domain: TimeD, FreqD, or TimeD_FreqD
• Scenario: FDay or MDay

Total Dataset: 60 .mat files (10 users × 6 data variations each)

🧠 Neural Network Architecture

Pre-Optimized Network Features:

• Architecture: Standard feedforward neural network
• Hidden Layers: Fixed architecture
• Activation Functions: Default MATLAB configurations
• Training Algorithm: Standard backpropagation

Post-Optimized Network Features:

• Dynamic Architecture: Adaptive hidden layer sizing using multiple heuristics
• Optimized Training: Enhanced training algorithms with early stopping
• Advanced Metrics: Comprehensive authentication performance evaluation
• Regularization: Improved generalization techniques

Key Optimization Techniques:

1. Dynamic Neuron Calculation:

   • Geometric pyramid rule
   • Input size-based rule
   • Sample size-based rule
   • Median-based final selection

2. Training Enhancements:

   • Monitoring and early stopping
   • Adaptive learning rates
   • Cross-validation

3. Performance Metrics:

   • False Acceptance Rate (FAR)
   • False Rejection Rate (FRR)
   • Equal Error Rate (EER)
   • Area Under Curve (AUC)
   • F1-Score

🚀 Getting Started

Prerequisites

• MATLAB R2019b or later
• Neural Network Toolbox
• Statistics and Machine Learning Toolbox

Installation & Setup

1. Clone or download the project repository
2. Open MATLAB and navigate to the project directory
3. Ensure all required toolboxes are installed

Running the System

Option 1: Pre-Optimized Network

cd '(02)-Pre-optimized-Neural-Network'
main

Option 2: Post-Optimized Network

cd '(03)-Post-optimized-Neural-Network'
main

Option 3: Data Analysis Only

cd '(01)-Data-Analysis'
analyze_inter_variance
analyze_intra_variance

Interactive Menu Options

When running the main scripts, you'll be prompted to select:

1. Time domain - Analysis using temporal features
2. Frequency domain - Analysis using spectral features
3. Combined - Analysis using both time and frequency features

📊 Performance Evaluation

The system evaluates authentication performance using industry-standard metrics:

Primary Metrics:

• Accuracy: Overall classification accuracy
• FAR (False Acceptance Rate): Rate of incorrectly accepting impostors
• FRR (False Rejection Rate): Rate of incorrectly rejecting genuine users
• EER (Equal Error Rate): Point where FAR equals FRR
• AUC (Area Under Curve): ROC curve area measurement
• F1-Score: Harmonic mean of precision and recall

Visualization:

• Confusion matrices for each user
• ROC curves
• Performance comparison charts
• Training progress monitoring

🔍 Key Features

Data Analysis:

• Inter-variance Analysis: Examines differences between users
• Intra-variance Analysis: Examines consistency within users
• Statistical Profiling: Comprehensive feature statistics

Neural Network Training:

• Adaptive Architecture: Dynamic hidden layer sizing
• Multi-domain Support: Time, frequency, and combined features
• Cross-validation: Robust performance estimation
• Early Stopping: Prevents overfitting

Evaluation Framework:

• Comprehensive Metrics: Multiple performance indicators
• User-specific Analysis: Individual user performance profiling
• Comparative Analysis: Pre vs. post-optimization comparison
• Visual Reports: Automated result visualization

📈 Results and Analysis

Expected Outcomes:

• Improved Authentication Accuracy: Post-optimized networks typically achieve 85-95% accuracy
• Reduced Error Rates: Lower FAR and FRR compared to pre-optimized versions
• Better Generalization: Enhanced performance on cross-day scenarios
• Optimized Architecture: Automatically tuned network parameters

Performance Benchmarks:

• Time Domain: Good for temporal patterns
• Frequency Domain: Effective for spectral characteristics
• Combined Domain: Best overall performance
• Same Day: Higher accuracy due to reduced environmental variations
• Cross Day: More challenging but realistic scenario

🛠️ Technical Implementation

Core Components:

1. Data Loading (load_data.m)

• Automated data file discovery
• Structured data organization
• Memory-efficient loading

2. Network Creation (create_network.m)

• Dynamic architecture calculation
• Configurable parameters
• Binary classification setup

3. Training Engine (train_network.m)

• Adaptive training algorithms
• Progress monitoring
• Early stopping mechanisms

4. Evaluation System (calculate_metrics.m)

• Comprehensive metric calculation
• Statistical analysis
• Performance visualization

Advanced Features:

• Modular Design: Easy to extend and modify
• Error Handling: Robust error management
• Logging: Comprehensive execution logging
• Reproducibility: Consistent results across runs