Computer Sciences and Information Technology
Title : | Reconfigurable and Machine Learning Attack Resilient Spintronics based Physically Unclonable Function Design for constrained IoT Systems |
Area of research : | Computer Sciences and Information Technology |
Focus area : | Cybersecurity, Spintronics |
Principal Investigator : | Dr. Deepika Gupta, Dr. Shyama Prasad Mukherjee International Institute Of Information Technology, Chhattisgarh |
Timeline Start Year : | 2023 |
Timeline End Year : | 2026 |
Contact info : | deepika@iiitnr.edu.in |
Details
Executive Summary : | "The Internet of Things (IoT) devices often face resource constraints, with limited computations, low area, and battery budgets. Current cryptographic key-based authentication protocols are not lightweight enough to address these challenges, and stored cryptographic keys in non-volatile memories are highly vulnerable to invasive attacks. Physically unclonable functions (PUFs) are emerging as a hardware security tool for implementing keyless security strategies. PUFs leverage innate manufacturing variations during IC fabrication and produce unique keys on-the-fly to avoid storing cryptographic keys in non-volatile memory. However, CMOS based PUFs require large post-processing units, large area, and energy consumption overheads. Spintronic devices have attracted attention due to their nonvolatility, 3-D incorporation, and scalability. However, spintronics transfer torque magnetic tunnel junction (STT-MTJ) still suffers from issues like long incubation time, high switching current densities, and read current disturbance. A novel switching mechanism, voltage gated spin orbit torque (SOT), has been developed to address these challenges, improving switching reliability and lower switching energy consumption.
This project aims to design energy-efficient and ultra-lightweight spintronics PUF design for resource-constrained IoT, verifying its vulnerability against side-channel attacks and machine learning-based modeling attacks. The project develops test-setups to derive performance metrics and robustness against power side-channel attacks and machine learning attacks, which can be used for other PUF designs. The designs, processes, and test setups developed can be made available to the research community through a website." |
Co-PI: | Dr. Japa Aditya, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana-500075 |
Total Budget (INR): | 18,08,370 |
Organizations involved