Towards Implementation of Robust and Low-Cost Security Primitives for Resource-Constrained IoT Devices

TL;DR

This paper presents new hardware-based security primitives, including PUFs and TRNGs, tailored for resource-constrained IoT devices to enhance security cost-effectively and reliably under environmental variations.

Contribution

It introduces novel DRAM-based PUF and TRNG techniques optimized for low-cost, resource-limited IoT environments, addressing environmental effects and silicon aging.

Findings

DRAM-based PUF provides reliable authentication under environmental variations.

DRAM remanence and startup value methods generate high-quality random numbers.

Power supply noise-based TRNG offers cost-effective, infinite random bit generation.

Abstract

In recent years, due to the trend in globalization, system integrators have had to deal with integrated circuit (IC)/intellectual property (IP) counterfeiting more than ever. These counterfeit hardware issues counterfeit hardware that have driven the need for more secure chip authentication. High entropy random numbers from physical sources are a critical component in authentication and encryption processes within secure systems [6]. Secure encryption is dependent on sources of truly random numbers for generating keys, and there is a need for an on chip random number generator to achieve adequate security. Furthermore, the Internet of Things (IoT) adopts a large number of these hardware-based security and prevention solutions in order to securely exchange data in resource efficient manner. In this work, we have developed several methodologies of hardware-based random functions in order to address the issues and enhance the security and trust of ICs: a novel DRAM-based intrinsic Physical Unclonable Function (PUF) [13] for system level security and authentication along with analysis of the impact of various environmental conditions, particularly silicon aging; a DRAM remanence based True Random Number Generation (TRNG) to produce random sequences with a very low overhead; a DRAM TRNG model using its startup value behavior for creating random bit streams; an efficient power supply noise based TRNG model for generating an infinite number of random bits which has been evaluated as a cost effective technique; architectures and hardware security solutions for the Internet of Things (IoT) environment. Since IoT devices are heavily resource constrained, our proposed designs can alleviate the concerns of establishing trustworthy and security in an efficient and low-cost manner.