Biometric Models: Behavioral Authentication

Overview

This section details the behavioral biometric models that enable continuous, passive authentication throughout user sessions. Unlike physiological biometrics (face, voice) that require active user participation, behavioral biometrics monitor user patterns during normal interaction with the system. These models analyze keystroke dynamics and human activity patterns to build a comprehensive behavioral profile.

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Keystroke Dynamics: LSTM-Based Authentication

Introduction

Keystroke dynamics analyzes the unique typing patterns of individuals, including the timing between keystrokes and how long keys are held down. This behavioral biometric operates continuously in the background, providing non-intrusive authentication without disrupting user workflow.

Keystroke Features

Key Timing Features:

Feature Type	Description	Formula	Uniqueness
Hold Time (Dwell)	Duration key is pressed	Release_time - Press_time	Typing pressure and habit
Flight Time	Time between consecutive keys	Next_press - Current_release	Typing rhythm
Digraph Latency	Time for specific key pairs	Duration between two specific keys	Finger movement patterns
Down-Down (DD)	Press to next press	Next_press - Current_press	Overall typing speed
Up-Up (UU)	Release to next release	Next_release - Current_release	Key release patterns

Example Digraph Features: For typing "AB":

• DU.A.A: Hold time for key A
• DU.B.B: Hold time for key B
• DD.A.B: Time from press A to press B
• UD.A.B: Time from release A to press B
• UU.A.B: Time from release A to release B

Model Architecture

Architecture Summary:

Layer	Type	Output Shape	Parameters	Purpose
Input	Time Series	(31, 128)	0	Hold/flight time sequences
LSTM-1	Recurrent	(31, 128)	66,560	Capture short-term typing patterns
Dropout-1	Regularization	(31, 128)	0	Prevent overfitting
LSTM-2	Recurrent	(64,)	49,408	Learn long-term typing habits
Dropout-2	Regularization	(64,)	0	Prevent overfitting
Dense	Fully Connected	(32,)	2,080	Feature compression
Output	SoftMax	(51,)	1,683	User classification

Total Parameters: 119,731

Why LSTM for Keystroke Dynamics?

Design Rationale:

1. Sequential Nature: Typing is inherently sequential; order and timing matter
2. Long-term Dependencies: LSTM remembers typing habits across entire sequences
3. Temporal Patterns: Captures both immediate rhythm and sustained typing style
4. Variable Length: Handles different text lengths effectively

LSTM Advantages:

Aspect	Traditional ML	LSTM
Feature Engineering	Manual extraction required	Automatic from sequences
Context Window	Fixed, limited	Adaptive, full sequence
Temporal Dependencies	Weak	Strong
Typing Rhythm	Partial capture	Complete capture
User Adaptability	Static	Dynamic over time

Data Preprocessing

Dataset: CMU DSL-StrongPasswordData

• Users: 51 individuals
• Sessions: Multiple typing sessions per user
• Password: Same password typed by all users
• Features: Hold times and flight times for all key combinations

Preprocessing Pipeline:

Processing Steps:

1. Feature Extraction:

   • Hold time: Key press duration for each key
   • Flight time: Time between consecutive key releases and presses

2. Normalization:

   • StandardScaler: Mean = 0, Std = 1
   • Prevents bias toward features with larger scales

3. Sequence Creation:

   • Sliding window approach for temporal context
   • Overlapping sequences for better coverage

4. Label Encoding:

   • Convert user IDs to numerical labels (0-50)

Training Pipeline

Training Configuration:

Parameter	Value	Rationale
Batch Size	32	Balance between speed and stability
Epochs	50	With early stopping
Learning Rate	1e-3	Standard for Adam optimizer
Optimizer	Adam	Adaptive learning rate
Loss Function	Sparse Categorical Crossentropy	Multi-class classification
Validation Split	20%	Monitor generalization

Training Callbacks:

• EarlyStopping: Monitor validation loss, patience of 10 epochs
• ReduceLROnPlateau: Reduce learning rate by 0.5 when loss plateaus for 5 epochs
• ModelCheckpoint: Save best model based on validation accuracy

Training Progress:

Epoch	Training Accuracy	Training Loss	Validation Accuracy	Validation Loss
1	6.22%	3.5714	15.01%	2.9623
2	15.74%	2.9303	25.25%	2.6214
3	26.13%	2.5719	36.27%	2.1881
4	36.13%	2.1787	44.79%	1.8937
5	43.85%	1.9134	49.45%	1.7391
6	47.72%	1.7864	54.53%	1.5605
Final	83.00%	0.6234	83.00%	0.6891

Evaluation Metrics

1. Classification Accuracy

Test Set Performance:

• Accuracy: 83.00%
• Precision (weighted): 82.50%
• Recall (weighted): 83.00%
• F1-Score (weighted): 82.75%

2. Confusion Matrix Analysis

The confusion matrix reveals:

• Strong diagonal values indicating correct classifications
• Minimal confusion between most users
• Some overlap for users with similar typing patterns

Interpretation:

• High diagonal values: Model correctly identifies most users
• Off-diagonal values: Occasional misclassification (expected for similar typing styles)
• Overall pattern: Clear separation between users

3. ROC-AUC Analysis

Per-Class Performance:

• Mean AUC across all users: 0.92
• Best performing users: AUC > 0.95
• Challenging users: AUC between 0.85-0.90

The ROC curves demonstrate strong discriminative ability across different thresholds, with most classes showing excellent separation.

4. Learning Curves

Observations:

• Rapid initial improvement (first 10 epochs)
• Steady convergence toward 83% accuracy
• Minimal gap between training and validation (good generalization)
• No significant overfitting detected

Implementation Details

Enrollment Process:

Verification Process:

Verification Algorithm:

def verify_keystroke(keystroke_sequence, user_id, threshold=0.75):
    """
    Verify user identity from keystroke pattern
    
    Args:
        keystroke_sequence: Sequence of hold/flight times
        user_id: Claimed identity
        threshold: Minimum confidence for acceptance
    
    Returns:
        risk_level: 'low', 'medium', or 'high'
        confidence: Model's confidence score
    """
    # Preprocess sequence
    features = extract_features(keystroke_sequence)
    normalized = scaler.transform(features)
    
    # Get prediction
    predictions = model.predict(normalized)
    confidence = predictions[user_id]
    
    # Determine risk level
    if confidence >= threshold:
        risk_level = 'low'
    elif confidence >= threshold * 0.6:
        risk_level = 'medium'
    else:
        risk_level = 'high'
    
    return risk_level, confidence

Challenges and Variability

Intra-User Variability:

Typing patterns can vary for the same user due to:

Factor	Impact	Mitigation
Fatigue	Slower typing, longer hold times	Adaptive thresholds
Stress	Irregular rhythm, more errors	Confidence scoring
Multitasking	Interrupted sequences	Sequence filtering
Time of Day	Morning vs evening differences	Time-aware modeling
Device Type	Desktop vs laptop keyboard	Device-specific profiles

Inter-User Similarity:

Some users may have similar typing patterns:

• Similar typing speed
• Comparable experience level
• Shared training background (e.g., same typing course)

Mitigation Strategies:

1. Continuous Learning: Update user profiles over time
2. Confidence Thresholds: Adjust based on historical performance
3. Multi-Factor Integration: Combine with other biometrics for ambiguous cases
4. Context Awareness: Consider device, time, and application context

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Human Activity Recognition: CNN-GRU Hybrid

Introduction

Human Activity Recognition (HAR) classifies physical activities based on sensor data from mobile devices. By monitoring activities like walking, sitting, standing, and running, the system adds an additional layer of behavioral verification that complements keystroke dynamics.

Activity Types

The system recognizes six distinct activities:

Sensor Data Sources

Mobile Sensors Utilized:

Sensor	Measurements	Sample Rate	Purpose
Accelerometer	Linear acceleration (x, y, z)	50 Hz	Detect movement patterns
Gyroscope	Angular velocity (x, y, z)	50 Hz	Capture rotational motion
Total Body Acc	Total acceleration	50 Hz	Overall body dynamics

Data Segmentation:

• Window Size: 2.56 seconds (128 readings at 50 Hz)
• Overlap: 50% (1.28 seconds)
• Features per Window: 561 engineered features

Feature Engineering

Feature Categories:

1. Time Domain (384 features):

   • Mean, standard deviation, min, max
   • Median absolute deviation
   • Interquartile range
   • Signal magnitude area
   • Energy measure
   • Correlation coefficients

2. Frequency Domain (177 features):

   • FFT coefficients
   • Spectral energy
   • Frequency domain entropy
   • Peak frequency
   • Frequency skewness and kurtosis

Model Architecture

Architecture Summary:

Layer	Type	Output Shape	Parameters	Purpose
Input	Features	(30, 19, 1)	0	Reshaped sensor features
Conv1D-1	Convolutional	(28, 64)	256	Local pattern extraction
Conv1D-2	Convolutional	(26, 64)	12,352	Hierarchical features
MaxPooling	Pooling	(13, 64)	0	Dimensionality reduction
Flatten	Reshape	(832,)	0	Prepare for LSTM
LSTM	Recurrent	(32,)	110,720	Temporal dependencies
Dropout	Regularization	(32,)	0	Prevent overfitting
Dense-1	Fully Connected	(32,)	1,056	Feature refinement
Output	Sigmoid	(6,)	198	Activity classification

Total Parameters: 124,582

Why CNN-GRU Hybrid?

Design Rationale:

Component	Strength	Application
CNN	Spatial pattern recognition	Local feature extraction from multi-axis sensors
LSTM/GRU	Temporal sequence modeling	Capture activity transitions and duration
Hybrid	Best of both worlds	Spatial and temporal feature integration

Architecture Benefits:

1. CNN extracts local patterns from multi-dimensional sensor data
2. LSTM captures temporal dependencies across time windows
3. Combined approach handles both spatial and temporal aspects of movement

Training Pipeline

Dataset: UCI HAR (Human Activity Recognition)

• Subjects: 30 volunteers (19-48 years)
• Activities: 6 daily activities
• Training Samples: 7,352
• Test Samples: 2,947
• Features: 561 per sample

Data Preprocessing:

Training Configuration:

Parameter	Value	Rationale
Batch Size	16	Small for better gradient estimation
Epochs	30	Sufficient convergence
Learning Rate	1e-3	Default for RMSprop
Optimizer	RMSprop	Good for recurrent networks
Loss Function	Categorical Crossentropy	Multi-class classification
Validation Split	Test set provided	Standard UCI HAR split

Evaluation Metrics

1. Classification Performance

Test Set Results:

• Accuracy: 89.89%
• Loss: 0.656

Per-Class Performance:

Activity	Precision	Recall	F1-Score	Support
Walking	93.75%	93.95%	93.85%	496
Walking Upstairs	90.89%	90.87%	90.88%	471
Walking Downstairs	86.55%	99.52%	92.58%	420
Sitting	85.71%	54.55%	66.67%	491
Standing	94.75%	75.10%	83.82%	532
Laying	95.15%	95.15%	95.15%	537

2. Confusion Matrix

                 Predicted
              0    1    2    3    4    5
Actual 0    465   31    0    0    0    0   (Walking)
       1     10  428   33    0    0    0   (Walking Upstairs)
       2      2    0  418    0    0    0   (Walking Downstairs)
       3      0    2   48   60    0    0   (Sitting)
       4      0    0  132  399    0    0   (Standing)
       5      0   26    0    0    0  511   (Laying)

Key Observations:

• Excellent performance on dynamic activities (walking, laying)
• Confusion between sitting and standing (expected due to similarity)
• Walking variants sometimes confused (upstairs vs downstairs)
• Overall strong diagonal with minimal off-diagonal confusion

3. Activity-Specific Insights

High Accuracy Activities:

• Laying (95.15%): Most distinct sensor pattern
• Walking (93.85%): Clear periodic motion
• Walking Upstairs (90.88%): Distinct acceleration pattern

Challenging Activities:

• Sitting vs Standing: Similar static postures, low movement
• Walking Variants: Upstairs/downstairs require subtle distinction

Implementation Details

Real-Time Inference:

Verification Process:

def verify_activity(sensor_window, user_profile, threshold=0.80):
    """
    Verify user based on activity pattern
    
    Args:
        sensor_window: 561-dimensional feature vector
        user_profile: Historical activity distribution
        threshold: Minimum similarity for low risk
    
    Returns:
        risk_level: Activity-based risk assessment
        activity: Predicted current activity
    """
    # Predict activity
    activity_probs = model.predict(sensor_window)
    predicted_activity = np.argmax(activity_probs)
    confidence = activity_probs[predicted_activity]
    
    # Compare with user profile
    similarity = compare_activity_distribution(
        activity_probs, 
        user_profile
    )
    
    # Assess risk
    if similarity >= threshold:
        risk_level = 'low'
    elif similarity >= threshold * 0.6:
        risk_level = 'medium'
    else:
        risk_level = 'high'
    
    return risk_level, ACTIVITIES[predicted_activity]

Challenges and Limitations

Current Challenges:

1. Device Variability:

   • Different phone models have different sensor characteristics
   • Sensor placement varies (pocket, hand, bag)
   • Calibration differences between devices

2. Environmental Factors:

   • Terrain affects walking patterns (flat vs incline)
   • Footwear impacts gait
   • Carrying items changes movement dynamics

3. User Variability:

   • Physical condition affects activity patterns
   • Age and fitness level influence movement
   • Temporary conditions (injury, illness) alter behavior

Mitigation Strategies:

1. Device Normalization: Calibrate for specific device types
2. Adaptive Profiles: Update user baselines over time
3. Context Awareness: Consider time, location, historical patterns
4. Confidence Scoring: Use uncertainty for ambiguous cases

Future Enhancements

Proposed Improvements:

1. Transfer Learning:

   • Pre-train on large public datasets
   • Fine-tune on user-specific data
   • Domain adaptation for new devices

2. Advanced Architectures:

   • Attention mechanisms for key motion segments
   • Bidirectional LSTM for better temporal modeling
   • Residual connections for deeper networks

3. Extended Activities:

   • Add cycling, driving, running
   • Recognize custom user activities
   • Continuous vs sporadic activity distinction

4. Real-Time Optimization:

   • Model quantization for mobile deployment
   • Edge computing for reduced latency
   • Battery-efficient inference strategies

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Integration with Risk Classification

Both keystroke dynamics and human activity recognition feed into the Random Forest risk classifier:

Combined Behavioral Profile:

• Keystroke patterns provide authentication during desk work
• Activity patterns verify user during mobile usage
• Together, they create comprehensive behavioral coverage

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