Securing ZigBee Commercial Communications Using Constellation Based Distinct Native Attribute Fingerprinting

This work provides development of Constellation Based DNA (CB-DNA) Fingerprinting for use in systems employing quadrature modulations and includes network protection demonstrations for ZigBee offset quadrature phase shift keying modulation. Results are based on 120 unique networks comprised of seven authorized ZigBee RZSUBSTICK devices, with three additional like-model devices serving as unauthorized rogue devices. Authorized network device fingerprints are used to train a Multiple Discriminant Analysis (MDA) classifier and Rogue Rejection Rate (RRR) estimated for 2520 attacks involving rogue devices presenting themselves as authorized devices. With MDA training thresholds set to achieve a True Verification Rate (TVR) of TVR = 95% for authorized network devices, the collective rogue device detection results for SNR ≥ 12 dB include average burst-by-burst RRR ≈ 94% across all 2520 attack scenarios with individual rogue device attack performance spanning 83.32% \u3c RRR \u3c 99.81%

Physical Layer Discrimination of Electronic Control Units Using Wired Signal Distinct Native Attribute (WS-DNDA)

The Controller Area Network (CAN) bus is a communication system used in automobiles to connect the electronic components required for critical vehicle operations. These components are called Electronic Control Units (ECU) and each one exercises one or more functions within the vehicle. ECUs can provide autonomous safety features and increased comfort to drivers but these advancements may come at the expense of vehicle security. Researchers have shown that the CAN bus can be hacked by compromising authorized ECUs or by physically connecting unauthorized devices to the bus. Physical layer (PHY) device fingerprinting has emerged as one of the accepted approaches to establishing vehicle security. This paper uses a fingerprinting method called Wired Signal Distinct Native Attribute (WS-DNA) and classification algorithm called Multiple Discriminant Analysis Maximum Likelihood (MDA/ML) to achieve ECU discrimination which includes device classification and verification