Research Agenda
Advancing the science of wireless detection through applied research in threat identification, sensor design, and machine learning.
TraceLock Labs conducts applied research at the intersection of wireless security, distributed sensing, and decision support. Our research agenda is organized around three primary areas, each contributing to the broader goal of converting environmental signals into actionable security intelligence.
Wireless Threat Detection
Research into methods for identifying unauthorized wireless devices, covert transmitters, and surveillance equipment across multiple frequency bands. This includes developing detection signatures for known threat categories and anomaly detection approaches for novel or unknown devices.
Focus areas: rogue access point detection, Bluetooth surveillance identification, cellular IMSI catcher detection, IoT device enumeration.
Sensor Architectures
Design and evaluation of distributed sensor network topologies for physical security environments. Research examines how sensor placement, communication protocols, and data aggregation strategies affect detection coverage and reliability.
Focus areas: mesh sensor networks, edge computing for signal processing, sensor fusion algorithms, environmental resilience.
AI-Assisted RF Classification
Machine learning approaches for automated identification and classification of RF signals. Research focuses on training models to distinguish between benign and potentially hostile wireless activity, reducing false positives, and improving classification accuracy in noisy environments.
Focus areas: signal fingerprinting, supervised classification models, anomaly scoring, transfer learning for new device types.
Research outputs from TraceLock Labs serve as signal inputs to the Secure Decision Operating System (SDOS), where they are evaluated within a structured decision architecture.