My research focus is on Hardware and AI-enabled Cyber Security in computer systems, specifically the Internet of Things (IoT). The primary focus of my doctoral studies was on intelligent circuits and architectures utilizing emerging nanoscale electronic devices. In particular, I investigated the utilization of spintronic devices for improving IoT edge devices’ and memory systems’ performance and reliability. These include circuit and algorithmic innovations spanning from initial analog to digital signal conversion up through heterogeneous technology reconfigurable computing fabrics to achieve highly scalable and energy-efficient computing chips. My primary research interests are:
- Hardware and AI-enabled Security in IoT
- Neuromorphic and Biologically-Inspired AI Hardware
- Energy-Efficient and Intelligent Signal Conversion and Processing in IoT
- Reconfigurable and Adaptive Computer Architectures
- Low-Power and Reliability-Aware VLSI circuits
- Emerging Spin-Based Devices
Selected Research Projects
Ongoing: Researching novel and effective approaches for securing the Internet of Things (IoT) Supply Chain vulnerabilities
- Investigate security vulnerabilities within the IoT supply chain and demonstrate the innovative use of supervised and unsupervised AI approaches to detect and prevent attacks on the IoT hardware supply chain.
- Investigate possible hardware trojan insertion, firmware attack, and counterfeit component attack on Printed Circuit Boards (PCBs) within the IoT supply chain.
- Demonstrate Firmware Attack coNstruction and Deployment on powEr Management IC (FANDEMIC) and its impacts on bare-metal IoT Applications.
- Investigate security vulnerabilities that exist within the Autonomous Vehicles’ hardware.
- Trojan Resilience Untrusted Cell Library Analysis, Detection, and Mitigation.
Ongoing: Researching novel and effective approaches for securing the Internet of Things (IoT) hardware against cutting edge Deep-learning-based Power Side-channel Attacks (DeePSAs) using emerging devices
- Demonstrate innovative use of supervised and unsupervised deep learning approaches to develop the proposed Deep-learning-based Power Side-channel Attack (DeePSA), which can accurately estimate the encryption key from sampled power traces even in the presence of process variation, signal noise, and perturbation.
- Demonstrate utilization of commercially available STT-MRAM to design energy and area-optimized Look-Up Tables (LUTs) for the encryption hardware to mitigate the proposed DeePSA, called Secure Hardware for IoT using Emerging-devices against side-channeL Deep-learning attacks (SHIELD).