Department of Electronic Engineering, Gachon University, Seongnam 13120, Republic of Korea.
Department of Mathematics and Big-Data Science, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.
Sensors (Basel). 2022 Dec 11;22(24):9709. doi: 10.3390/s22249709.
Ultrasound systems have been widely used for consultation; however, they are susceptible to cyberattacks. Such ultrasound systems use random bits to protect patient information, which is vital to the stability of information-protecting systems used in ultrasound machines. The stability of the random bit must satisfy its unpredictability. To create a random bit, noise generated in hardware is typically used; however, extracting sufficient noise from systems is challenging when resources are limited. There are various methods for generating noises but most of these studies are based on hardware. Compared with hardware-based methods, software-based methods can be easily accessed by the software developer; therefore, we applied a mathematically generated noise function to generate random bits for ultrasound systems. Herein, we compared the performance of random bits using a newly proposed mathematical function and using the frequency of the central processing unit of the hardware. Random bits are generated using a raw bitmap image measuring 1000 × 663 bytes. The generated random bit analyzes the sampling data in generation time units as time-series data and then verifies the mean, median, and mode. To further apply the random bit in an ultrasound system, the image is randomized by applying exclusive mixing to a 1000 × 663 ultrasound phantom image; subsequently, the comparison and analysis of statistical data processing using hardware noise and the proposed algorithm were provided. The peak signal-to-noise ratio and mean square error of the images are compared to evaluate their quality. As a result of the test, the min entropy estimate (estimated value) was 7.156616/8 bit in the proposed study, which indicated a performance superior to that of GetSystemTime. These results show that the proposed algorithm outperforms the conventional method used in ultrasound systems.
超声系统已广泛用于会诊，但它们容易受到网络攻击。这些超声系统使用随机位来保护患者信息，这对超声机中使用的信息保护系统的稳定性至关重要。随机位的稳定性必须满足其不可预测性。为了生成随机位，通常使用硬件产生的噪声；然而，在资源有限的情况下，从系统中提取足够的噪声是具有挑战性的。有各种生成噪声的方法，但这些研究大多基于硬件。与基于硬件的方法相比，基于软件的方法可以被软件开发者轻松访问；因此，我们应用了一种数学生成的噪声函数来为超声系统生成随机位。在这里，我们比较了使用新提出的数学函数和使用硬件中央处理器频率生成的随机位的性能。随机位是使用大小为 1000×663 字节的原始位图图像生成的。生成的随机位分析生成时间单位中的采样数据作为时间序列数据，然后验证平均值、中位数和模式。为了将随机位进一步应用于超声系统，通过对 1000×663 的超声幻影图像进行独占混合来随机化图像；随后，提供了使用硬件噪声和提出的算法进行统计数据处理的比较和分析。比较了图像的峰值信噪比和均方误差，以评估它们的质量。测试结果表明，在提出的研究中，估计值为 7.156616/8 位的最小熵估计值优于 GetSystemTime 的性能。这些结果表明，所提出的算法优于超声系统中使用的传统方法。
