RF Anomaly Detection

Establishes a statistical norm profile for each authenticated device by observing its emissions over time. This profile captures not just the device's RF fingerprint, but also its typical transmission patterns, power levels, and modulation usage.

• Uses Gaussian mixture models to define acceptable parameter ranges
• Continuously updates to account for environmental drift
• Flags any transmission that falls outside the multivariate normal distribution

A device suddenly transmitting at an unexpected power level or with a shifted carrier frequency offset triggers an alert, even if its core fingerprint remains valid.

The critical engine that distinguishes between benign temporal variation and malicious activity. Hardware impairments naturally drift due to temperature and aging, but spoofing attempts introduce abrupt, non-linear changes.

• Applies Kalman filtering to track and predict legitimate component drift
• Uses cumulative sum (CUSUM) algorithms to detect abrupt statistical shifts
• Prevents false positives from normal oscillator warm-up behavior

This prevents the system from locking out a legitimate device simply because its power amplifier has heated up during a long transmission session.

Analyzes the joint probability of multiple signal parameters deviating simultaneously. A single anomaly might be noise; correlated anomalies across independent hardware components are a high-confidence threat indicator.

• Correlates I/Q imbalance, carrier frequency offset, and phase noise deviations
• Identifies spoofing attempts where an attacker correctly mimics one impairment but fails on others
• Uses copula models to capture non-linear dependencies between features

An impersonator might replicate a device's frequency offset but cannot simultaneously match its unique phase noise profile and power amplifier non-linearity.

Identifies previously unseen or unknown emitter types that do not match any existing authorized device profile. This is essential for detecting rogue transmitters and unauthorized hardware introduced into a secure environment.

• Employs one-class support vector machines trained only on authorized devices
• Uses autoencoder neural networks to reconstruct normal signals and flag high reconstruction error
• Detects new modulation schemes or protocol violations in real time

Critical for military and critical infrastructure deployments where any unknown emitter represents a potential threat.

Normalizes anomaly scores to compensate for multipath fading, Doppler shift, and other channel effects that can distort the received signal and mimic hardware anomalies.

• Applies channel equalization before feature extraction
• Uses domain-adversarial training to make anomaly detectors robust to varying propagation conditions
• Separates channel-induced distortion from genuine hardware behavioral changes

Prevents a mobile device entering a high-multipath environment from being falsely flagged as anomalous.

Monitors the sequential pattern of transmissions, not just individual signal characteristics. A device that transmits at unusual intervals, with atypical packet lengths, or in an unexpected order triggers an alert.

• Applies long short-term memory (LSTM) networks to learn normal transmission cadence
• Detects replay attacks by identifying repeated, out-of-sequence signal patterns
• Flags denial-of-sleep attacks that attempt to drain device batteries through abnormal polling

This layer catches protocol-level attacks that would bypass pure physical-layer fingerprint analysis.