EAGER: SARE: Real-Time Learning and Countering of Side-Channel Emissions to Enable Secure RF and Analog Microelectronics

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Rapid growth of sensors and Internet of Things (IoT) has the potential to transform the society, economy, and improve the quality of life. Many IoT devices at the extreme cloud edge collect and transmit sensitive information wirelessly for remote computing. However, the sensitive information can be leaked through side channels, including power consumption and electromagnetic (EM) emissions. The vulnerability of those wireless devices to hacking or exploitation has emerged as a major public concern over IoT security and safety. Nevertheless, existing state-of-the-art cybersecurity approaches are mainly focused on software and digital modules. Security measures have not been integrated in the radio frequency (RF) and analog domains to verify signal and power sources or to suppress the side-channel emissions. To bridge the gap, this project will develop a holistic self-testing approach incorporating nanoscale electromagnetic sensing devices, reconfigurable RF circuits, and machine-learning algorithms to detect threats and counter malicious attacks directly at the RF and analog front-end. Combing emerging material, device, circuit, and system concepts, this project aims to develop a built-in threat-detection-and-reaction approach in the RF/analog domain without degrading the performance while achieving good energy efficiency. The results from this work have the potential to make a significant impact on the secure electronics and telecommunication industry. The minimal usage of energy and space can allow the energy-constrained wireless devices to have an on-chip detection-and-reaction system to rapidly predict and counter malicious attacks in the front line. In addition, the project will provide a unique opportunity for students of different levels (K-12, undergraduate, and graduate) and the general public to learn the security vulnerability of wireless devices and its countermeasures. The goal of this project is to enhance security in wireless devices at the RF/analog front-end through interdisciplinary research. Low-power and low-voltage on-chip sensors will be developed to collect data for analysis of power and EM signal behaviors. To sense small changes of magnetic fields and inform the machine-learning circuits, a nanoscale heterostructure will be developed to monolithically integrate CMOS circuits with novel spin-torque devices that can be utilized as robust high-fidelity EM sensors and embedded into interconnects. A sensing circuit will be developed to achieve high-accuracy EM measurement while eliminating any response to the external stray field disturbance. Low-power charge-sharing analog-to-digital converters will be investigated to perform machine-learning algorithms. In order to rapidly predict and detect the potential attacks, machine-learning circuits will be trained to learn the normal behavior of the system with different operation schemes so that this built-in detection system can enable accurate predictions of a variety of adversaries in real time. Learning algorithms and reconfigurable architectures will be developed to form a closed-loop fast threat-detection-and-reaction system. Boosting approaches for ensembles of linear classifiers will be exploited to enhance the detection accuracy with minimal hardware costs. Programmable nanoscale oscillators with emerging materials for frequency hopping and various switching schemes will be exploited to increase resistance and resilience to tampering side-channel attacks. When a threat is detected, the suppression mechanism will be activated to mitigate the damage immediately. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.