• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.6
• /
• pp.1105-1114
• /
• 2021

Due to the recent surge in popularity of cryptocurrency, the threat of cryptojacking, a malicious code for mining cryptocurrencies, is increasing. In particular, web-based cryptojacking is easy to attack because the victim can mine cryptocurrencies using the victim's PC resources just by accessing the website and simply adding mining scripts. The cryptojacking attack causes poor performance and malfunction. It can also cause hardware failure due to overheating and aging caused by mining. Cryptojacking is difficult for victims to recognize the damage, so research is needed to efficiently detect and block cryptojacking. In this work, we take representative distinct symptoms of cryptojacking as an indicator and propose a new architecture. We utilized the K-Nearst Neighbors(KNN) model, which trained computer performance indicators as behavior-based dynamic analysis techniques. In addition, a K-means model, which trained the frequency of malicious script words for script similarity-based static analysis techniques, was utilized. The KNN model had 99.6% accuracy, and the K-means model had a silhouette coefficient of 0.61 for normal clusters.

• Journal of the Institute of Convergence Signal Processing
• /
• v.22 no.3
• /
• pp.99-103
• /
• 2021

Recently, convolutional neural networks (CNNs) have been widely utilized due to their outstanding performance in various computer vision fields. However, due to their computational-intensive and high memory requirements, it is difficult to deploy CNNs on hardware platforms that have limited resources, such as mobile devices and IoT devices. To address these limitations, a neural network compression research is underway to reduce the size of neural networks while maintaining their performance. This paper proposes a CNN compression technique that dynamically adjusts the thresholds of pruning, one of the neural network compression techniques. Unlike the conventional pruning that experimentally or heuristically sets the thresholds that determine the weights to be pruned, the proposed technique can dynamically find the optimal thresholds that prevent accuracy degradation and output the light-weight neural network in less time. To validate the performance of the proposed technique, the LeNet was trained using the MNIST dataset and the light-weight LeNet could be automatically obtained 1.3 to 3 times faster without loss of accuracy.

• Journal of Broadcast Engineering
• /
• v.28 no.3
• /
• pp.333-336
• /
• 2023

Recently, the transformer model used in natural language processing is also applied to the image super resolution field, showing good performance. However, these transformer based models have a disadvantage that they are difficult to use in small mobile devices because they are complex and have many learning parameters and require high hardware resources. Therefore, in this paper, we propose a knowledge distillation technique that can effectively reduce the size of a transformer based super resolution model. As a result of the experiment, it was confirmed that by applying the proposed technique to the student model with reduced number of transformer blocks, performance similar to or higher than that of the teacher model could be obtained.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.12
• /
• pp.1-7
• /
• 2023

The advent of deep learning technologies has led to the development of various medical applications, making healthcare services more convenient and effective. Among these applications, heart rate estimation is considered a vital method for assessing an individual's health. Traditional methods, such as photoplethysmography through smart watches, have been widely used but are invasive and require additional hardware. Recent advancements allow for contactless heart rate estimation through facial image analysis, providing a more hygienic and convenient approach. In this paper, we propose a lightweight methodology capable of accurately estimating heart rate in mobile environments, using a specialized 2-channel network structure based on 2D convolution. Our method considers both subtle facial movements and color changes resulting from blood flow and muscle contractions. The approach comprises two major components: an Encoder for analyzing image features and a regression layer for evaluating Blood Volume Pulse. By incorporating both features simultaneously our methodology delivers more accurate results even in computing environments with limited resources. The proposed approach is expected to offer a more efficient way to monitor heart rate without invasive technology, particularly well-suited for mobile devices.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.6
• /
• pp.1125-1132
• /
• 2023

The importance of networks is gradually expanding in the battlefield environment. As time goes by, the types of tactical data links used in the Korean JTDLS are increasing, and the military's weapon systems equipped with tactical data link systems are increasing. Thorough quality verification is required to provide stable software to the wider battlefield. This study examines how to prepare an environment in which various simulation tests to verify the stability of the Korean JTDLS project can be conducted as diverse as possible using minimal physical space and Hardware resources. Through this, it is possible to improve the completeness of the project and secure the stability of the program, and it is intended to contribute to securing higher stability and reliability by securing maximum test capabilities in a limited test environment even in Linux based system project of a similar environment.

• Journal of Internet of Things and Convergence
• /
• v.10 no.2
• /
• pp.77-84
• /
• 2024

Over the past few years, IoT edges have begun to emerge based on new low-latency communication protocols such as 5G. However, IoT edges, despite their enormous advantages, pose new complementary threats, requiring new security solutions to address them. In this paper, we propose a cloud environment-based IoT edge architecture model that complements IoT systems. The proposed model acts on machine learning to prevent security threats in advance with network traffic data extracted from IoT edge devices. In addition, the proposed model ensures load and security in the access network (edge) by allocating some of the security data at the local node. The proposed model further reduces the load on the access network (edge) and secures the vulnerable part by allocating some functions of data processing and management to the local node among IoT edge environments. The proposed model virtualizes various IoT functions as a name service, and deploys hardware functions and sufficient computational resources to local nodes as needed.

• Geophysics and Geophysical Exploration
• /
• v.8 no.4
• /
• pp.262-269
• /
• 2005

Since the buried cultural relics are three-dimensional (3-D) objects in nature, 3-D survey is more preferable in archeological exploration. 3-D Ground Penetrating Radar (GPR) survey based on very dense data in principle, however, might need much higher cost and longer time of exploration than other geophysical methods commonly used for the archeological exploration, such as magnetic and electromagnetic methods. We developed a small-scale continuous data acquisition system which consists of two sets of GPR antennas and the precise positioning device tracking the moving-path of GPR antenna automatically and continuously. Since the high cost of field work may be partly attributed to establishing many profile lines, we adopted a concept of data acquisition at arbitrary locations not along the pre-established profile lines. Besides this hardware system, we also developed several software packages in order to effectively process and visualize the 3-D data obtained by the developed system and the data acquisition concept. Using the developed system, we performed 3-D GPR survey to investigate the possible historical remains of Baekje Kingdom at Buyeo city, South Korea, prior to the excavation. Owing to the newly devised system, we could obtain 3-D GPR data of this survey area having areal extent over about $17,000m^2$ within only six-hours field work. Although the GPR data were obtained at random locations not along the pre-established profile lines, we could obtain high-resolution 3-D images showing many distinctive anomalies, which could be interpreted as old agricultural lands, waterways, and artificial structures or remains. This cast: history led us to the conclusion that 3-D GPR method is very useful not only to examine a small anomalous area but also to investigate the wider region of the archeological interests.

• Journal of The Korean Association of Information Education
• /
• v.5 no.3
• /
• pp.415-431
• /
• 2001

Recently, the biggest interest in education, education using ICT(Information and Communication Technology) is being emphasized excessively and only the practical side is being embossed on the research about it, so it is causing worry that it is inclined to technical use. In this study, we tried to prepare the strategy for efficient use of ICT in search of theoretical level on use of ICT as an alternative plan for these problems. First, we defined the concept of efficient use of ICT and fixed high thinking of human as basic authority cited for deduction of strategy and analyzed Integrated Thinking Model of Iowa State Dept. of Education. We categorized synthetic thinking for efficient use of ICT through these works. In addition, we classified ICT for efficient use into software area, hardware area, and use skill area, and observed each concepts and interrelationship. And we argued 'Decision Authority of Relation between Thinking Area and ICT Area' to examine relation between synthetic thinking category and each area of ICT, and we established concretization of that, 'Analysis Matrix for Deduction of ICE-EUS'. We tried to guarantee the propriety of deduction process and the clearness of deduction result through this works. Through this process we finally deduct the ICT-EUS(Efficient-Use Strategy of ICT) about learning resources, learning tools, tutee, searching, communication, production and presentation of ICT area. ICT-EUS is expected to provide possibility of being able to enhance the efficiency and effectiveness of achievement of learning goals through cognitive analysis about learning resources, tools and use skills beyond simple use of ICT.

• Land and Housing Review
• /
• v.5 no.3
• /
• pp.137-149
• /
• 2014

The authors derived rental housing policy measures that are appropriate for the current conditions of Korean housing supply and demand based on the confirmation of the issues of Korean rental housing system and reviewing implications from review of cases of foreign countries and these measures can be categorized into linkage with the urban regeneration and multi-functional development, acquisition of financial resources, operational management, policy and institutional aspects. For the expansion of supply of rental housing, it is essential to link the rental housing policy with urban regeneration. To pursue regeneration of underdeveloped areas and expansion of supply of rental housing in line with urban regeneration, more development sites should be added. Further, the rental home policy must be integrated into a new paradigm that includes securing commercial viability and providing various residential conveniences through multi-functional development. In addition, diversification of developers of real estates turning away from the existing framework of policy that has been focused only on the state-led housing supply so that local governments and private sector players can take part in. Next, new options for funding the supply of rental housing must be sought. First, raising financial resources sequentially through cyclical development approach could be considered. Or, various funding schemes including utilizing Tax-increment financing (TIF) based on the local tax revenues that will be accrued after the development projects and supply of rental housing. Or there should be various schemes to raise funds including utilization of TIFs that are based on the revenues that will be realized after the development projects and supply of rental housing, or utilizing REITs where funds can be provided through private sector investments. Also, getting out from the planning practice that focused only on physical expansion of supply of rental housing, continual operational management must be performed even after the development. These activities must be supported through establishment of control tower at the national level and continuous attention must be paid even after the development by developing specialized operational management companies that are led by private sector players. Finally, in addition to the hardware support that is focused on the public rental housing only, software support such as conditional provision of housing voucher or tax exemption for low-income classes should be provided, too. In other words, a shift from policies that are supplier-centric to ones that are customer-centric must take place.

• Geophysics and Geophysical Exploration
• /
• v.23 no.2
• /
• pp.72-88
• /
• 2020

Recent rapid advances in computer hardware performance have led to relatively low computational costs, increasing the number of applications of machine-learning techniques to geophysical problems. In particular, deep-learning techniques are gaining in popularity as the number of cases successfully solving complex and nonlinear problems has gradually increased. In this paper, applications of seismic data denoising methods using deep-learning techniques are introduced and investigated. Depending on the type of attenuated noise, these studies are grouped into denoising applications of coherent noise, random noise, and the combination of these two types of noise. Then, we investigate the deep-learning techniques used to remove the corresponding noise. Unlike conventional methods used to attenuate seismic noise, deep neural networks, a typical deep-learning technique, learn the characteristics of the noise independently and then automatically optimize the parameters. Therefore, such methods are less sensitive to generalized problems than conventional methods and can reduce labor costs. Several studies have also demonstrated that deep-learning techniques perform well in terms of computational cost and denoising performance. Based on the results of the applications covered in this paper, the pros and cons of the deep-learning techniques used to remove seismic noise are analyzed and discussed.