This document outlines the comprehensive security architecture of the LockLess biometric authentication system.

• Security Overview
• Threat Model
• Security Principles
• Encryption Architecture
• Biometric Security
• Key Management
• Template Storage
• Liveness Detection
• Access Control
• Audit Logging
• Platform Security
• Security Testing
• Compliance
• Security Best Practices

LockLess implements a multi-layered security architecture designed to protect biometric data and ensure system integrity. The system follows the principle of "defense in depth" with multiple security controls at each layer.

┌─────────────────────────────────────────────────────────┐
│                    Application Layer                    │
│  • Input Validation  • Access Control  • Audit Logging │
├─────────────────────────────────────────────────────────┤
│                    Biometric Layer                      │
│  • Liveness Detection • Anti-Spoofing  • Quality Check │
├─────────────────────────────────────────────────────────┤
│                    Encryption Layer                     │
│  • AES-256 Encryption • Key Derivation • Secure Storage│
├─────────────────────────────────────────────────────────┤
│                    Platform Layer                       │
│  • TPM Integration   • Secure Boot     • Memory Protection│
└─────────────────────────────────────────────────────────┘

• Threat: Unauthorized access to biometric templates
• Mitigation: AES-256 encryption, secure key management
• Detection: Audit logging, access monitoring

• Threat: Presentation attacks using photos, videos, or masks
• Mitigation: Multi-modal liveness detection
• Detection: Real-time anti-spoofing algorithms

• Threat: Replaying captured biometric data
• Mitigation: Challenge-response mechanisms, timestamp validation
• Detection: Session management, nonce validation

• Threat: Reconstructing original biometric data from templates
• Mitigation: One-way feature extraction, template protection
• Detection: Template integrity checks

• Threat: Extracting information through timing or power analysis
• Mitigation: Constant-time algorithms, secure coding practices
• Detection: Performance monitoring

• Threat: Gaining unauthorized system access
• Mitigation: Principle of least privilege, access controls
• Detection: Audit logging, anomaly detection

• Never Trust, Always Verify: All access requests are verified
• Least Privilege: Users have minimum required permissions
• Continuous Monitoring: All activities are logged and monitored

• Data Minimization: Only collect necessary biometric data
• Purpose Limitation: Use data only for intended purposes
• Retention Limitation: Delete data when no longer needed
• Local Processing: All processing happens on-device

• Multiple Controls: Security controls at every layer
• Fail Secure: System fails to secure state
• Redundancy: Multiple security mechanisms
• Layered Defense: Each layer provides additional protection

• Secure Configuration: Default settings are secure
• Minimal Attack Surface: Reduce exposed functionality
• Secure Coding: Follow secure coding practices
• Regular Updates: Keep system and dependencies updated

• Algorithm: Advanced Encryption Standard with 256-bit keys
• Mode: Galois/Counter Mode (GCM) for authenticated encryption
• Key Size: 256 bits
• IV Size: 96 bits
• Tag Size: 128 bits

• Algorithm: PBKDF2 (Password-Based Key Derivation Function 2)
• Hash Function: SHA-256
• Iterations: 100,000 (configurable)
• Salt Size: 256 bits

from cryptography.hazmat.primitives.ciphers.aead import AESGCM
from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
from cryptography.hazmat.primitives import hashes
import os

class BiometricEncryption:
    """AES-256-GCM encryption for biometric templates."""

    def __init__(self):
        self.algorithm = AESGCM
        self.key_size = 32  # 256 bits
        self.iv_size = 12   # 96 bits
        self.tag_size = 16  # 128 bits

    def derive_key(self, password: str, salt: bytes) -> bytes:
        """Derive encryption key from password."""
        kdf = PBKDF2HMAC(
            algorithm=hashes.SHA256(),
            length=self.key_size,
            salt=salt,
            iterations=100000
        )
        return kdf.derive(password.encode())

    def encrypt_template(self, template: bytes, password: str) -> bytes:
        """Encrypt biometric template."""
        # Generate random salt and IV
        salt = os.urandom(32)
        iv = os.urandom(12)

        # Derive key
        key = self.derive_key(password, salt)

        # Encrypt template
        cipher = self.algorithm(key)
        encrypted_data = cipher.encrypt(iv, template, None)

        # Combine salt + IV + encrypted data
        return salt + iv + encrypted_data

    def decrypt_template(self, encrypted_data: bytes, password: str) -> bytes:
        """Decrypt biometric template."""
        # Extract components
        salt = encrypted_data[:32]
        iv = encrypted_data[32:44]
        ciphertext = encrypted_data[44:]

        # Derive key
        key = self.derive_key(password, salt)

        # Decrypt template
        cipher = self.algorithm(key)
        return cipher.decrypt(iv, ciphertext, None)

• One-Way Process: Original biometric data cannot be reconstructed
• Irreversible: Templates cannot be reversed to original images
• Unique: Each template is unique to the individual
• Stable: Templates remain consistent over time

@dataclass
class BiometricTemplate:
    """Secure biometric template structure."""
    user_id: str
    features: np.ndarray  # 512-dimensional feature vector
    quality_score: float
    creation_time: datetime
    template_id: str  # Unique template identifier
    version: int  # Template format version

1. Texture Analysis

   • Analyze skin texture patterns
   • Detect artificial materials
   • Check for printing artifacts

2. Depth Analysis

   • Use depth information if available
   • Detect flat surfaces
   • Verify 3D structure

3. Motion Analysis

   • Detect natural eye movement
   • Check for blinking patterns
   • Verify head movement

4. Spectral Analysis

   • Analyze light reflection
   • Detect screen refresh patterns
   • Check for camera artifacts

class LivenessDetector:
    """Multi-modal liveness detection."""

    def __init__(self):
        self.texture_model = load_texture_model()
        self.depth_model = load_depth_model()
        self.motion_model = load_motion_model()

    def check_liveness(self, face_image: np.ndarray,
                      depth_map: Optional[np.ndarray] = None,
                      motion_sequence: Optional[List[np.ndarray]] = None) -> bool:
        """Comprehensive liveness check."""
        scores = []

        # Texture analysis
        texture_score = self.texture_model.predict(face_image)
        scores.append(texture_score)

        # Depth analysis (if available)
        if depth_map is not None:
            depth_score = self.depth_model.predict(depth_map)
            scores.append(depth_score)

        # Motion analysis (if available)
        if motion_sequence is not None:
            motion_score = self.motion_model.predict(motion_sequence)
            scores.append(motion_score)

        # Combine scores
        final_score = np.mean(scores)
        return final_score > self.liveness_threshold

Master Password
       │
       ▼
   PBKDF2-SHA256
       │
       ▼
   Derived Key
       │
       ▼
   Template Encryption Key

• Encrypted Storage: Keys stored encrypted at rest
• Memory Protection: Keys cleared from memory after use
• Access Control: Keys protected by OS-level permissions
• Key Rotation: Regular key rotation (configurable)

class TPMKeyManager:
    """TPM-based key management."""

    def __init__(self):
        self.tpm_available = self._check_tpm_availability()

    def generate_key(self, key_id: str) -> bytes:
        """Generate key using TPM."""
        if not self.tpm_available:
            raise SecurityError("TPM not available")

        # TPM key generation implementation
        pass

    def store_key(self, key_id: str, key: bytes) -> bool:
        """Store key in TPM."""
        # TPM key storage implementation
        pass

• File Format: Custom encrypted format
• File Extension: .enc (encrypted)
• Directory Structure: Organized by user ID
• Access Permissions: OS-level file permissions

templates/
├── user1/
│   ├── template_001.enc
│   ├── template_002.enc
│   └── metadata.json
├── user2/
│   ├── template_001.enc
│   └── metadata.json
└── system/
    ├── config.enc
    └── keys.enc

• Checksums: SHA-256 checksums for each template
• Digital Signatures: HMAC-based integrity verification
• Version Control: Template format versioning
• Backup Verification: Regular integrity checks

class TemplateIntegrity:
    """Template integrity verification."""

    def verify_template(self, template_path: str, expected_checksum: str) -> bool:
        """Verify template integrity."""
        with open(template_path, 'rb') as f:
            data = f.read()

        actual_checksum = hashlib.sha256(data).hexdigest()
        return actual_checksum == expected_checksum

    def generate_checksum(self, template_data: bytes) -> str:
        """Generate template checksum."""
        return hashlib.sha256(template_data).hexdigest()

• Skin Texture: Analyze natural skin patterns
• Material Detection: Detect artificial materials
• Print Quality: Check for printing artifacts
• Resolution Analysis: Verify image resolution

• 3D Structure: Verify facial 3D structure
• Depth Maps: Use depth information if available
• Surface Normals: Analyze surface orientation
• Curvature Analysis: Check facial curvature

• Eye Movement: Detect natural eye movement
• Blinking: Verify blinking patterns
• Head Movement: Check for natural head movement
• Facial Expressions: Detect micro-expressions

• Light Reflection: Analyze light reflection patterns
• Screen Detection: Detect screen refresh patterns
• Camera Artifacts: Check for camera-specific artifacts
• Frequency Analysis: Analyze frequency domain

class AdvancedLivenessDetector:
    """Advanced multi-modal liveness detection."""

    def __init__(self):
        self.models = {
            'texture': TextureLivenessModel(),
            'depth': DepthLivenessModel(),
            'motion': MotionLivenessModel(),
            'spectral': SpectralLivenessModel()
        }
        self.weights = {
            'texture': 0.4,
            'depth': 0.3,
            'motion': 0.2,
            'spectral': 0.1
        }

    def detect_liveness(self, data: Dict[str, Any]) -> LivenessResult:
        """Comprehensive liveness detection."""
        scores = {}

        # Texture analysis
        if 'face_image' in data:
            scores['texture'] = self.models['texture'].analyze(data['face_image'])

        # Depth analysis
        if 'depth_map' in data:
            scores['depth'] = self.models['depth'].analyze(data['depth_map'])

        # Motion analysis
        if 'motion_sequence' in data:
            scores['motion'] = self.models['motion'].analyze(data['motion_sequence'])

        # Spectral analysis
        if 'face_image' in data:
            scores['spectral'] = self.models['spectral'].analyze(data['face_image'])

        # Weighted combination
        final_score = sum(scores[modality] * self.weights[modality]
                         for modality in scores)

        return LivenessResult(
            is_live=final_score > self.threshold,
            confidence=final_score,
            modality_scores=scores
        )

• Admin Access: Full system control
• User Access: Limited to own data
• Service Access: API access only
• Guest Access: Read-only access

• Template Access: Biometric template access
• Configuration Access: System configuration access
• Log Access: Audit log access
• API Access: REST API access

class AccessControl:
    """Role-based access control."""

    ROLES = {
        'admin': ['*'],  # All permissions
        'user': ['read_own_templates', 'update_own_profile'],
        'service': ['api_access', 'read_config'],
        'guest': ['read_public_info']
    }

    def check_permission(self, user_role: str, permission: str) -> bool:
        """Check if user has permission."""
        if user_role not in self.ROLES:
            return False

        user_permissions = self.ROLES[user_role]
        return '*' in user_permissions or permission in user_permissions

• Authentication Events: Login attempts, successes, failures
• Enrollment Events: User enrollment, template creation
• Access Events: Data access, configuration changes
• Security Events: Security violations, suspicious activity
• System Events: System startup, shutdown, errors

{
  "timestamp": "2025-01-04T10:30:00Z",
  "event_type": "authentication_attempt",
  "user_id": "john_doe",
  "success": true,
  "ip_address": "192.168.1.100",
  "user_agent": "LockLess/1.0.0",
  "additional_data": {
    "confidence": 0.92,
    "processing_time": 0.35
  },
  "security_level": "info"
}

• Encryption: Sensitive log data encrypted
• Integrity: Log integrity verification
• Access Control: Restricted log access
• Retention: Configurable log retention

• Real-time Monitoring: Continuous log monitoring
• Anomaly Detection: Unusual pattern detection
• Alert System: Security alert generation
• Forensic Analysis: Post-incident analysis

• Windows Biometric Framework: Integration with WBF
• Windows Hello: Native Windows Hello support
• Credential Provider: Custom credential provider
• Group Policy: Enterprise policy support

• Real-time Protection: Windows Defender integration
• Threat Detection: Malware detection
• Behavioral Analysis: Suspicious behavior detection
• Quarantine: Automatic threat quarantine

• Mandatory Access Control: SELinux/AppArmor policies
• Process Isolation: Process-level security
• File System Protection: File system access control
• Network Security: Network access control

• Service Management: Systemd service integration
• Security Context: Secure service execution
• Resource Limits: Resource usage limits
• Dependency Management: Service dependencies

• Hardware Security Module: Android Keystore integration
• Key Attestation: Key attestation support
• Biometric Prompt: Android BiometricPrompt integration
• Fingerprint API: Fingerprint authentication support

• App Sandboxing: Application sandboxing
• Permission Model: Android permission system
• Verified Boot: Verified boot integration
• Security Updates: Automatic security updates

• Security Functions: Test security-critical functions
• Encryption/Decryption: Test cryptographic operations
• Input Validation: Test input validation
• Error Handling: Test error conditions

• End-to-End Security: Test complete security flows
• API Security: Test API security
• Database Security: Test data storage security
• Network Security: Test network communications

• Vulnerability Assessment: Identify security vulnerabilities
• Exploit Testing: Test for exploitable vulnerabilities
• Social Engineering: Test human factors
• Physical Security: Test physical access controls

• Accuracy Testing: Test biometric accuracy
• Spoofing Resistance: Test anti-spoofing measures
• Performance Testing: Test security performance
• Compatibility Testing: Test platform compatibility

class SecurityTestSuite:
    """Comprehensive security test suite."""

    def test_encryption_security(self):
        """Test encryption implementation."""
        # Test key derivation
        # Test encryption/decryption
        # Test key management
        pass

    def test_template_protection(self):
        """Test template protection."""
        # Test template encryption
        # Test template integrity
        # Test template access control
        pass

    def test_liveness_detection(self):
        """Test liveness detection."""
        # Test anti-spoofing measures
        # Test presentation attack detection
        # Test false acceptance/rejection rates
        pass

    def test_access_control(self):
        """Test access control."""
        # Test role-based access control
        # Test permission enforcement
        # Test privilege escalation prevention
        pass

• Information Security Management: ISMS implementation
• Risk Management: Security risk assessment
• Security Controls: Implementation of security controls
• Continuous Improvement: Ongoing security improvement

• Identify: Asset and risk identification
• Protect: Security control implementation
• Detect: Threat detection capabilities
• Respond: Incident response procedures
• Recover: Recovery and restoration procedures

• Data Protection: Personal data protection
• Privacy by Design: Privacy-first design
• Data Subject Rights: User rights implementation
• Data Breach Notification: Breach notification procedures

• FIDO2: WebAuthn and CTAP support
• UAF: Universal Authentication Framework
• U2F: Universal Second Factor
• Biometric Standards: Biometric authentication standards

class ComplianceManager:
    """Compliance management system."""

    def __init__(self):
        self.standards = {
            'iso27001': ISO27001Compliance(),
            'nist': NISTCompliance(),
            'gdpr': GDPRCompliance(),
            'fido': FIDOCompliance()
        }

    def check_compliance(self, standard: str) -> ComplianceResult:
        """Check compliance with specific standard."""
        if standard not in self.standards:
            raise ValueError(f"Unknown standard: {standard}")

        return self.standards[standard].check_compliance()

    def generate_compliance_report(self) -> ComplianceReport:
        """Generate comprehensive compliance report."""
        results = {}
        for standard, checker in self.standards.items():
            results[standard] = checker.check_compliance()

        return ComplianceReport(results)

• Input Validation: Validate all inputs
• Output Encoding: Encode all outputs
• Error Handling: Secure error handling
• Memory Management: Secure memory management

• Security Review: Security-focused code review
• Static Analysis: Automated static analysis
• Dynamic Analysis: Runtime security analysis
• Dependency Scanning: Third-party dependency scanning

• Security Testing: Comprehensive security testing
• Penetration Testing: Regular penetration testing
• Vulnerability Scanning: Automated vulnerability scanning
• Red Team Exercises: Simulated attack exercises

• Secure Defaults: Secure default configurations
• Configuration Validation: Validate all configurations
• Change Management: Controlled configuration changes
• Documentation: Comprehensive configuration documentation

• Security Monitoring: Continuous security monitoring
• Log Management: Centralized log management
• Alert System: Automated security alerts
• Incident Response: Rapid incident response

• Principle of Least Privilege: Minimum required permissions
• Regular Access Reviews: Periodic access reviews
• Multi-Factor Authentication: MFA for all access
• Session Management: Secure session management

• Regular Updates: Regular security updates
• Patch Management: Systematic patch management
• Vulnerability Management: Vulnerability tracking and remediation
• Dependency Updates: Third-party dependency updates

• Secure Backups: Encrypted backup storage
• Recovery Testing: Regular recovery testing
• Disaster Recovery: Comprehensive disaster recovery plan
• Business Continuity: Business continuity planning

• Security Training: Regular security training
• Awareness Programs: Security awareness programs
• Incident Response Training: Incident response training
• Phishing Simulation: Phishing simulation exercises

This security architecture ensures that LockLess provides enterprise-grade security while maintaining usability and performance. Regular security assessments and updates ensure the system remains secure against evolving threats.