One means by which the security of Internet-of-Things (IoT)-enabled devices may be augmented is through radio-frequency fingerprinting-based authentication methods. As variability in CMOS processes increases with technology scaling, the hardware imperfections that form RF fingerprints can be controlled with small reconfigurable elements, enabling the feasibility of RF fingerprinting as a low overhead security measure for device authentication. To achieve rapid RF identification, we present an inherently secure RF power amplifier and a convolutional neural network-based machine learning classifier through an exploration of combinatorial randomness and self-aware detection mechanisms. By selecting different subsets of thinly sliced power amplifier elements, combinations of random process variations are exploited and updated to form a large search space of distinct RF fingerprints and improve fingerprint prominence. The rich features enabled by augmented device primitives are updated in a time-varying manner to strengthen built-in hardware security. Measurement results demonstrate the effectiveness of this approach at generating distinguishable RF fingerprints across a significant number of configurations.

Rapid growth of sensors and the Internet of Things is transforming society, the economy and the quality of life. Many devices at the extreme edge collect and transmit sensitive information wirelessly for remote computing. The device behavior can be monitored through side-channel emissions, including power consumption and electromagnetic (EM) emissions. This study presents a holistic self-testing approach incorporating nanoscale EM sensing devices and an energy-efficient learning module to detect security threats and malicious attacks directly at the front-end sensors. The built-in threat detection approach using the intelligent EM sensors distributed on the power lines is developed to detect abnormal data activities without degrading the performance while achieving good energy efficiency. The minimal usage of energy and space can allow the energy-constrained wireless devices to have an on-chip detection system to predict malicious attacks rapidly in the front line.

Real-time in-sensor spike sorting is a forefront requirement in the development of brainmachine interfaces (BMIs). This work presents the characterization, design, and efficient implementation on a field-programmable gate array (FPGA) of a novel approach to neural spike sorting intended for implantable devices based on convolutional neural networks (CNNs). While the temporal features, the shape of the spike signals, could be highly mitigated from the ambient noise, the proposed classifier effectively extracts spatial features from the multi-channel neural signal to maintain high accuracy on the noisy data. The proposed classifier mechanism was tested on real data that is recorded from multi-channel electrodes, containing 27 neural units, and the classifier achieves 93.1% accuracy despite high temporal noise in the signal. For hardware synthesis, the CNN weights are quantized to reduce the model storage requirement by 93% compared to its floating point-precision version, and the model achieves an accuracy of 86.1%.

A reconfigurable power amplifier (PA) is implemented in CMOS 65nm to enable radio identification for secure wireless communication by injecting tunable radio frequency fingerprints (RFFs) into the physical layer. The large ensemble of RFFs is achieved by offsetting the distributions of process variations affecting the PA’s hardware features. The resulting large RFF capacity is exploited to increase resilience to noise and temperature changes by selecting distinct RFFs from the ensemble and reconfiguring the PA to restore nominal RFFs following temperature shifts. The secure PA achieves over 14000 time-varying RFFs while consuming only 22 mW and occupying a core area of < 0.0951 mm2. A reinforcement learning (RL)-based control has been implemented on FPGA for closed-loop reconfiguration of the transmitter to achieve robust and low-overhead security measures that overcome noise and temperature influences in dynamic environments.

The growth of the IoT requires more comprehensive security measures than ever. RF fingerprinting (RFF) utilizes features in the signals and waveforms from transmitters’ physical-layer imperfections to classify and authenticate devices. To prevent attacks from impersonators, combinatorial randomness is exploited to augment the RF fingerprints with a high-efficiency PA for IoT applications. By enabling different subsets of thinly sliced PA elements, the transmitter can be reconfigured with 220 subsets that exhibit distinctive RF fingerprints for signal analysis at the edge. In this work, a combinatorial-randomness-based PA was implemented in a BLE system. The BLE packets’ in-phase and quadrature samples transmitted from each configuration are collected with different SNRs to emulate the environmental changes in communication channels. A lightweight convolutional neural network (CNN) classifier demonstrates the possibility of accurate and fast inference of unique features in the IoT environment, which our approach exploits to enable on-chip time-varying RF fingerprints.

With the advent of globalization, hardware trojans provide an ever-present threat to the security of devices. Much of the research to date has centered around documenting and providing detection methods for digital trojans. Few, however, have explored the space of trojans in the RF/analog front end. Two hardware trojans, an analytical analysis of the trojan impacts on two different types of amplifiers, and an unsupervised ML detection method for edge IOT applications using magnetic tunnel junction sensors for side-channel monitoring are explored. A classification autoencoder for anomaly detection is presented with an accuracy of greater than 90% with both single tone and BLE data is presented.