• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.45-55
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• 2020

Due to the development of information and communication technology, the number of new / variant malicious codes is increasing rapidly every year, and various types of malicious codes are spreading due to the development of Internet of things and cloud computing technology. In this paper, we propose a malware analysis method based on string information that can be used regardless of operating system environment and represents library call information related to malicious behavior. Attackers can easily create malware using existing code or by using automated authoring tools, and the generated malware operates in a similar way to existing malware. Since most of the strings that can be extracted from malicious code are composed of information closely related to malicious behavior, it is processed by weighting data features using text mining based method to extract them as effective features for malware analysis. Based on the processed data, a model is constructed using various machine learning algorithms to perform experiments on detection of malicious status and classification of malicious groups. Data has been compared and verified against all files used on Windows and Linux operating systems. The accuracy of malicious detection is about 93.5%, the accuracy of group classification is about 90%. The proposed technique has a wide range of applications because it is relatively simple, fast, and operating system independent as a single model because it is not necessary to build a model for each group when classifying malicious groups. In addition, since the string information is extracted through static analysis, it can be processed faster than the analysis method that directly executes the code.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1119-1127
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• 2018

Most of the cyber attacks are caused by malicious codes. The damage caused by cyber attacks are gradually expanded to IoT and CPS, which is not limited to cyberspace but a serious threat to real life. Accordingly, various malicious code analysis techniques have been appeared. Dynamic analysis have been widely used to easily identify the resulting malicious behavior, but are struggling with an increase in Anti-VM malware that is not working in VM environment detection. On the other hand, static analysis has difficulties in analysis due to various packing techniques. In this paper, we proposed malware classification techniques regardless of known packers or unknown packers through the proposed model. To do this, we designed a model of supervised learning and unsupervised learning for the features that can be used in the PE structure, and conducted the results verification through 98,000 samples. It is expected that accurate analysis will be possible through customized analysis technology for each class.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.97-106
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• 2020

Due to the rapid development and dissemination of 5G communication and IoT technologies, there are increasing demands for big data analysis techniques and service systems. In particular, explosively growing demands on AI technology adoption are also causing high competitions to take advantages of machine/deep-learning models to extract novel values from enormously collected data. In order to adopt AI technology to various research and application domains, it is necessary to prepare high-performance GPU-equipped systems and perform complicated settings to utilze deep learning models. To relieve the efforts and lower the barrier to utilize AI techniques, AIaaS(AI as a service) platform is attracting a great deal of attention as a promising on-line service, where the complexity of preparation and operation can be hidden behind the cloud side and service developers only need to utilize the high-level AI services easily. In this paper, we propose an AIaaS system which can support the creation of AI services based on Docker and OpenFaaS from the registration of models to the on-line operation. We also describe a case study to show how AI services can be easily generated by the proposed system.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.626-635
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• 2015

Along with the social interests and needs for health, the U-Healthcare Service as a new model to control individual dietary life and to prevent chronic diseases is now drawing the public attention. In this study, the characteristics of the requirement of U-Healthcare Service were investigated from subjects aged over 50 years old. As parts of the expectation to the U-Healthcare service, the customized clinical care & control (37.7%) and the systematic understanding of individual health state (21.4%) appeared significantly. And for the application of the U-Healthcare Service to the field of healthcare, the control of dietotherapy (43.9%) appeared higher than anything else and thus the customized service for individual dietotherapy seemed to be needed. Regarding the field of searching for health information, the disease control (35.6%), food materials (28.6%), and recipe for health foods (22.7%) appeared in sequence and revealed that the majority over 50% of subjects were interested in foods. The requirements for the information of dietary formula were also investigated through 5 points scaled questionnaires on respective topics of nutritive components, menus customized for corresponding health states (diseases), and the results obtained from surveys on respective topics all showed the needs for all information by representing each point value exceeded 3.9. Through the results obtained from this study, the provision of customized information enabling the nutritional control in accordance with individual states of health or disease was estimated to be needed for the popularization of U-Healthcare Service providing contents for dietary life of clients especially those of aged over 50 years.

• Journal of Digital Convergence
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• v.15 no.5
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• pp.129-138
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• 2017

Faced with the era of the IoT (Internet of Things) and smart homes, this study aims to explore the type of information loaded on smart devices that can lead to consumer's efficient energy use. 105 Americans participated in the survey with eight different versions according to two energy consumption levels (Above or Below condition) by four information frames (Finance, security, environment, or health). It was found that frames can make significant differences in consumers' attitudes; (1) Those in the Below condition worried about environments more than those in the Above condition; (2) Finance-framed information in the Above condition was the least effective to increase consumers' energy saving motivation; (3) In the Below condition, those receiving finance and security framed information revealed more environmental concerns than those receiving other types of informations. This study can contribute to the field by providing with basic research findings that smart device developers can refer to in the future. Also, follow-up studies need to be conducted to examine effective messages for Korean energy consumers.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.5
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• pp.177-188
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• 2018

The scale of graph data has been increased rapidly because of the growth of mobile Internet applications and the proliferation of social network services. This brings upon the imminent necessity of efficient distributed and parallel graph processing approach since the size of these large-scale graphs are easily over a capacity of a single machine. Currently, there are two popular parallel graph processing approaches, vertex-centric graph processing and block centric processing. While a vertex-centric graph processing approach can easily be applied to the parallel processing system, a block-centric graph processing approach is proposed to compensate the drawbacks of the vertex-centric approach. In these systems, the initial quality of graph partition affects to the overall performance significantly. However, it is a very difficult problem to divide the graph into optimal states at the initial phase. Thus, several dynamic load balancing techniques have been studied that suggest the progressive partitioning during the graph processing time. In this paper, we present a load balancing algorithms for the block-centric graph processing approach where most of dynamic load balancing techniques are focused on vertex-centric systems. Our proposed algorithm focus on an improvement of the graph partition quality by dynamically reassigning blocks in runtime, and suggests block split strategy for escaping local optimum solution.

• Journal of Convergence for Information Technology
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• v.7 no.4
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• pp.105-111
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• 2017

With the emergence of the fourth industrial revolution, more and more attempts have been made to apply IoT technology to the manufacturing process and launch the product. In this paper, we propose IoT authentication scheme based on hash chain which can easily apply IoT device to small and medium enterprises in Korea. In the proposed method, the companies that installed IoT devices suitable for the manufacturing environment are selected to maintain the linkage between IoT devices so that product information and release information can be efficiently collected and managed during the entire manufacturing process. In addition, the proposed scheme is characterized in that it does not require an additional encryption / decryption algorithm because the authentication information of the IoT device is constructed based on a hash chain. As a result of the performance evaluation, the efficiency of the manufacturing process was improved by 18.5% and the processing of the manufacturing process with the IoT device was shortened by 20.1% on the average according to the application of the IoT device. In addition, the labor cost reduction costs in the manufacturing process decreased by an average of 30.7%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1026-1037
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• 2016

3rd Generation Partnership Project (3GPP) is the progressing standardization of the narrowband IoT (NB-IoT) system to support massive devices for the Internet of Things (IoT) services. The NB-IoT system uses a coverage class technique to increase the performance of the NB-IoT system while serving massive devices in very wide coverage area. A moving device can change the coverage class according to the distance or the channel state between the base station and the moving device. However, in the conventional NB-IoT standard, the performance of the NB-IoT system degrades because the coverage class is changed based on the fixed criterion. This paper proposes the coverage class adaptation schemes to increase the performance of the NB-IoT system by dynamically change the coverage class according to the location or the channel state of the device. Simulation results show that the proposed coverage class adaptation scheme decreases both the signaling overhead and the PDCCH decoding error rate in comparison with the conventional coverage class adaptation scheme in the 3GPP standard.

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.373-383
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• 2021

Our society is aging rapidly. In this super-aged society, the increase in healthcare costs are considered a national problem that undermines the sustainability of social security. Various services for healthcare for the elderly have been promoted to address this. However, most of them have focused on healthcare after the outbreak of chronic diseases and lack preventive healthcare. Most of the preventive healthcare projects are only pilots. In this paper, the current status of health care services for senior citizens at home and abroad was analyzed and based on this, the limitations and improvements were analyzed to propose the establishment of IoT-based Total Silver Care Center. IoT-based Total Silver Care Center may be conveniently monitored the health status of the elderly through various sensors, medical devices, and smart bands. And based on this, it can improve the quality of nursing services through time-saving and work efficiency of nursing providers. In addition, health care interventions may be provided in a timely manner if there is a change in the health status of users. And real-time imaging systems can help overcome mental difficulties.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.5
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• pp.418-426
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• 2019

Recently, many studies on vital sign detection using a radar sensor related to Internet of Things(IoT) smart home systems have been conducted. Because vital signs such as respiration and cardiac rates generally cause micro-motions in the chest or back, the phase of the received echo signal from a target fluctuates according to the micro-motion. Therefore, vital signs are usually detected via spectral analysis of the phase. However, the probability of false alarms in cardiac rate detection increases as a result of various problems in the measurement environment, such as very weak phase fluctuations caused by the cardiac rate. Therefore, this study analyzes the difficulties of vital sign detection and proposes an efficient vital sign detection algorithm consisting of four main stages: 1) phase decomposition, 2) phase differentiation and filtering, 3) vital sign detection, and 4) reduction of the probability of false alarm. Experimental results using impulse-radio ultra-wideband radar show that the proposed algorithm is very efficient in terms of computation and accuracy.