DIM-Based Random Number Generation Using Quantum Noise Resources. (9th November 2022) - Wi, Hansaem; Lee, Seyoon; Yi, Okyeon
Abstract : Currently, unmanned aircraft systems (UASs) or drones are in service in various industrial fields, and each UAS operator establishes and operates their own independent drone system. These individual drone systems interact only with their own components without any integrated management. As the number of UASs is increasing due to the expansion of the drone industry, standardized operation is required. Therefore, to integrate and manage existing drone systems, the Federal Aviation Administration and National Aeronautics and Space Administration devised UAS Traffic Management (UTM). The drone identity module (DIM), which is being developed as a drone identification device, securely stores the remote identification (RID) of each drone and performs a cryptographic operation to secure information between the drone and UTM infrastructure. The DIM performs cryptographic authentication protocols to achieve cryptographic identification and authentication with the UTM infrastructure, which requires random numbers. Modern cryptographic systems rely on difficult computations, and an environment capable of generating secure cryptographic random numbers must be configured to provide high computational costs to attackers. In this paper, we explain the need for random numbers in the DIM, analyze random number generators used in related drone-based studies, and analyze the characteristics of noise resource generation devices that can be used in existing drone systems. Subsequently, based on the analysis results, existing methods are used to generate random numbers in the DIM, and limitations are derived. To overcome these limitations, we propose a method of generating random numbers in the DIM using quantum noise resources. For our proposal, we conduct an analysis of the physical specifications of noise resource generation devices, DIM prototypes, and quantum noise resource generators in existing drone systems, and we present the results of NIST 800-90B entropy measurement using data collected from quantum random number generators.