Continuous Authentication

This method authenticates a device by analyzing unique, unclonable hardware impairments in its transmitted radio frequency waveform. The system extracts features like I/Q imbalance, oscillator phase noise, and DAC non-linearity from every packet. Because these traits arise from microscopic manufacturing variances in analog components, they are impossible to spoof or replicate by an attacker, providing a robust Physical Unclonable Function (PUF) that operates independently of cryptographic keys.

A hardware security primitive that exploits inherent manufacturing variations in silicon to generate a unique, unclonable device fingerprint. PUFs derive cryptographic keys from microscopic physical disorder rather than storing them in memory, making them resistant to invasive attacks.

• SRAM PUF: Uses power-up state of SRAM cells as a fingerprint
• Arbiter PUF: Measures race conditions in signal propagation delays
• Provides the root of trust for continuous device authentication

A technique used to determine if a biometric sample originates from a live, present entity rather than a spoofed or replayed source. In continuous authentication, passive liveness checks run silently during sessions.

• Active liveness: Requires user interaction like blinking or head movement
• Passive liveness: Analyzes micro-textures, blood flow, or signal characteristics without user awareness
• Defeats presentation attacks using photos, masks, or recorded signals

A form of network attack where a valid data transmission is maliciously captured and retransmitted to impersonate a legitimate device. Continuous authentication systems must detect temporal anomalies that indicate replayed signals.

• Timestamp verification: Rejects messages outside acceptable time windows
• Nonce-based challenges: Requires fresh cryptographic responses per session
• RF fingerprinting defeats replay attacks because hardware impairments cannot be perfectly reproduced even from recorded signals

A critical biometric security metric measuring the likelihood that a system incorrectly authenticates an unauthorized user or device. For continuous authentication, FAR must be evaluated over extended time windows, not just at login.

• Represents a security breach — an impostor gains access
• Expressed as a percentage or ratio (e.g., 0.001%)
• Continuous systems compound risk: a 0.01% FAR per check becomes significant over thousands of checks
• Trades off against False Rejection Rate (FRR) — tightening one loosens the other

Algorithms that track and adjust for the slow temporal variation of hardware impairments caused by temperature changes, component aging, and environmental conditions. Without drift compensation, continuous authentication models degrade over time.

• Temperature compensation: Models normalize for thermal effects on oscillators and amplifiers
• Aging models: Predict how capacitor and transistor characteristics shift over months or years
• Adaptive thresholding: Dynamically adjusts acceptance boundaries as the reference signature evolves
• Prevents False Rejection Rate from climbing as devices age

A classification paradigm where the model must correctly identify known classes while rejecting unknown ones never seen during training. In continuous authentication, the system must recognize legitimate users while flagging novel impostors.

• Differs from closed-set classification which forces all inputs into known categories
• Uses confidence score thresholding to detect out-of-distribution samples
• Critical for zero-effort impostor detection — attackers who have never been enrolled
• Often combined with Out-of-Distribution (OOD) Detection techniques