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revention of Malware Installation in Dedicated Devices Built on General-Purpose Execution Environments
Doyeon Kim, Jione Choi, Kiseok Jeon, Wonjun Lee, Junghee Lee
http://doi.org/10.5626/JOK.2025.52.5.444
With digitalization of various industries, the demand for dedicated devices is increasing. Dedicated devices, such as digital banking branches, medical tablets, and educational tablets, are designed to perform specific tasks. Since they only run designated applications, they are them more secure with minimal the attack surface. Most of these devices are built on general-purpose execution environments like Android. Thus, they offer ease of development, usability, and high availability, contributing to their widespread adoption. At the same time, they may introduce new security vulnerabilities, necessitating security measures tailored to dedicated devices. This study analyed the vulnerabilities of dedicated devices operating in a general-purpose execution environment, evaluated the potential for vulnerabilities that could lead to malware installation, and proposed countermeasures. This research assumes that attackers do not have physical access to the device and that end users do not engage in malicious activities. The widely used Android environment was selected. Ten methods by which an attacker could remotely install malware on a Lenovo P11 device were identified. To mitigate these threats, a security mechanism optimized for dedicated devices was designed by implementing SELinux policies and installing a file integrity verification program.
Cardiovascular Disease Prediction using Single-Lead ECG Data
Chaeyoon Park, Gihun Joo, Suhwan Ji, Junbeom Park, Junho Baek, Hyeonseung Im
http://doi.org/10.5626/JOK.2024.51.10.928
The most representative approach to diagnosing cardiovascular disease is to analyze electrocardiogram (ECG), and most ECG data measured in hospitals consist of 12 leads. However, wearable healthcare devices usually measure only single-lead ECG, which has limitations in diagnosing cardiovascular disease. Therefore, in this paper, we conducted a study to predict common cardiovascular diseases such as atrial fibrillation (AF), left bundle branch block (LBBB), and right bundle branch block (RBBB) using a single lead that could be measured with a wearable healthcare device. For experiments, we used a convolutional neural network model and measured its performance using various leads in terms of AUC and F1-score. For AF, LBBB, and RBBB, average AUC values were 0.966, 0.971, and 0.965, respectively, and average F1-scores were 0.867, 0.816, and 0.848, respectively. These experimental results confirm the possibility of diagnosing cardiovascular disease using only a single lead ECG that can be obtained with wearable healthcare devices.
CSDVirt: An Emulator for Computational Storage Device
Ilkueon Kang, Jaehoon Shim, Jin-Soo Kim
http://doi.org/10.5626/JOK.2024.51.1.1
Since Computational Storage Device (CSD) concept was proposed, various forms of CSDs have been presented in both academia and industries. The standardization of CSD interfaces is currently undergoing, but they are still in a very early stage. As a result, the existing CSD proposals lack uniformity in interfaces and internal device architectures. This has led to significant engineering efforts for CSD research. In this paper, we propose CSDVirt to facilitate the CSD research and provide an environment similar to actual devices. CSDVirt is an emulator that offers CSD functionalities using NVMeVirt. With CSDVirt, the characteristics of various workloads on CSDs can be evaluated easily.
Device Status-Based Adaptive Frame Extraction and Streaming Control System to Block Obscene Videos in Mobile Devices
Jeongho Kang, Minsu Kim, Kwangsue Chung
http://doi.org/10.5626/JOK.2022.49.7.575
As the user’s access to video streaming services increases, technology for blocking obscene videos in mobile devices is attracting attention. However, the mobile device has a problem as a load is generated in the process of blocking obscene videos due to low processing power. In this paper, we propose a device status-based adaptive frame extraction and streaming control system to block obscene videos in mobile devices. The proposed system extracts frames based on the similarity comparison results between frames and changes in the obscenity of videos. In addition, similarity comparison and frame extraction are controlled according to the device status, and exposure of obscene videos is minimized through mosaic processing. Through the implementation result, it was confirmed that the proposed system improves the response performance to obscenity changes by about 40% through the adaptive frame extraction technology. In addition, it was confirmed that a load is generated in the process of blocking obscene videos by adaptively extracting frames according to the battery condition of the device.
Drunk Driving Detection System Using Wearable Devices
http://doi.org/10.5626/JOK.2022.49.5.397
Drunk driving may cause traffic accidents that result in human casualties. Even though most people are well aware of the danger, many traffic accidents still occur due to poor judgement caused by drinking. In this paper, we propose a drunk driving detection system using wearable devices. First, we use a smart watch, a wearable device, to collect data using only general-purpose sensors and sends the data via a smart phone to a server that performs machine learning to determine if the user is drinking. Then, the driver detection algorithm, which uses in-car beacons, sends a warning to the user to prevent drunk driving. We implemented the system on a smart watch, smart phone, and server, and also developed a practical user mobile app. The results showed that the accuracy of drinking detection and driver detection algorithms were around 92% and 99%, respectively.
A Streaming Control System for Real-time Blocking of Obscene Videos in Mobile Devices
Jeongho Kang, Minsu Kim, Kwangsue Chung
http://doi.org/10.5626/JOK.2021.48.8.966
As users’ accessibility to video streaming services increases, technology for real-time blocking of obscene videos in mobile devices is drawing attention. However, a load is generated for a mobile device during the blocking process due to a low processing power. In this paper, we propose a streaming control system for real-time blocking of obscene videos in mobile devices. The proposed system can extract the frame of video and analyze the obscenity of the video through an obscenity analysis engine. In addition, the load is minimized by determining the frame extraction method in consideration of obscenity change and similarity comparison results between frames, and obscene video is blocked by performing video mosaic processing. Through the implementation results, it was confirmed that the proposed system could minimize the load generated from a mobile device and user exposure to the obscene part.
Development and Application of Guidelines for Compliance with IEC 62304 International Standards for AI Medical Device Software
DongYeop Kim, Ye-Seul Park, Byungjeong Lee, Jung-Won Lee
http://doi.org/10.5626/JOK.2021.48.1.71
Medical device software developers must implement the processes required by IEC 62304, the international standard for medical device software life-cycle processes, and they must also have a large amount of artifacts to obtain a license. Recently, AI medical device software based on medical images has been actively developed, and since it is treated as standalone software, it must be approved in accordance with IEC 62304 for medical device software. The international standard for AI technology is currently in the discussion stage, and the developer should arbitrarily establish the life-cycle process of AI medical device software, and by matching the specifications of IEC 62304, the performance and safety of AI products will be evaluated. It is unclear which quality management technique should be used to produce the best artifact. This paper provides a quality control technique for fulfilling the scope and requirements of IEC 62304 compliance for AI medical device software in the form of guidelines. These guidelines are also applied to actual AI products to check their potential use in real applications.
Automatic Convolution Neural Network Model Compression Framework for Resource-Constrained Embedded Systems
Jonghun Jeong, Dasom Lee, Hyeonseok Jung, Hoeseok Yang
http://doi.org/10.5626/JOK.2020.47.2.136
Recently, attempts have been made to directly execute various convolutional neural network applications in resource-constrained embedded systems such as IoT. However, since embedded systems have limited computational capability and memory, the size of the neural network model that can be executed is restricted and may not satisfy real-time constraints. Therefore, in this paper, we propose a framework that automatically compresses a given neural network model to satisfy memory and execution time requirements and automatically generates code that can be executed on the target embedded system. Using the proposed framework, we demonstrate that the given neural network models can be automatically optimized for two STM32 Nucleo series boards with different HW specifications for various execution time and memory requirements.
An NVM-based Efficient Write-Reduction Scheme for Block Device Driver Performance Improvement
http://doi.org/10.5626/JOK.2019.46.10.981
Recently, non-volatile memory (NVRAM) has attracted substantial attention as a next-generation storage device due to the fact that it shows higher read/write performance than flash-based storage as well as higher cost-effectiveness than DRAM. One way to use NVRAM as a storage device is to modify the existing file system layer or block device layer. Leveraging the NVRAM block device driver is advantageous in terms of overall system compatibility, as it does not require any modification of the existing storage stack. However, when considering the byte-level addressing of the NVRAM device, the block write is not effective in terms of durability or performance. In this paper, we propose a block device driver that attempts to optimize the existing block write operations while considering the existing functionalities of the file system. The proposed block write reduction scheme provides a partial block write by classifying the type of blocks according to the structure of the file system as well as the amount of data modified in the block using XOR operation. Several experiments are performed to validate the performance of the proposed block device driver under various workloads, and the results show that, compared to the conventional block write operations, the amount of writes is reduced by up to 90%.
I/O Completion Technique of Virtualized System Considering CPU Usage with High-Performance Storage Devices
Hyeji Lee, Taehyung Lee, Minho Lee, Yongju Song, Young Ik Eom
http://doi.org/10.5626/JOK.2019.46.7.612
Recently, the advent of high-performance storage devices such as Samsung Z-SSD and Intel Optane SSD has shifted the I/O systems’ performance overhead from the storage devices to the software I/O layer. To optimize the I/O performance of high-performance storage devices, the hypervisor and operating system have focused on the effectiveness of polling technique, which is one of the I/O completion techniques applied in virtualized systems, and new techniques such as hybrid and adaptive polling are being adopted. This paper reveals the problem of the existing adaptive polling techniques provided by QEMU-KVM hypervisor and proposes a new I/O completion technique, which saves on CPU usage while fully utilizing high-performance storage devices. Our evaluation indicates that the proposed technique reduces CPU usage by up to 39.7% while delaying I/O latency to less than 5.3% only, in comparison to conventional systems.
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