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Young-Sik Jeong
Dongguk University

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Conference paper
Published: 05 January 2021 in Lecture Notes in Electrical Engineering
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Recently, Internet of Things (IoT) technologies have been fused with next-generation technologies such as 5G and deep learning and used in diverse fields such as smart homes, smart cars, and smart appliances. As the demand for IoT devices increases, security threats targeting IoT devices, IoT infrastructure, and IoT application programs have also been increasing. Diverse studies on IoT malware detection have been conducted to protect IoT devices particularly from IoT malware among the security threats. However, existing studies can only accurately detect known IoT malware, not new and variant IoT malware. In this study, the malware dynamic analysis (MALDA) scheme that accurately detects new and variant malware that threatens IoT devices quickly is proposed to reduce the damage caused to IoT devices. The MALDA scheme dynamically analyzes IoT malware in nested cloud environments by training the behavioral features of IoT malware based on the Convolutional Neural Network (CNN) model.

ACS Style

Jueun Jeon; Seungyeon Baek; Minho Kim; Inho Go; Young-Sik Jeong. IoT Malware Dynamic Analysis Scheme Using the CNN Model. Lecture Notes in Electrical Engineering 2021, 547 -553.

AMA Style

Jueun Jeon, Seungyeon Baek, Minho Kim, Inho Go, Young-Sik Jeong. IoT Malware Dynamic Analysis Scheme Using the CNN Model. Lecture Notes in Electrical Engineering. 2021; ():547-553.

Chicago/Turabian Style

Jueun Jeon; Seungyeon Baek; Minho Kim; Inho Go; Young-Sik Jeong. 2021. "IoT Malware Dynamic Analysis Scheme Using the CNN Model." Lecture Notes in Electrical Engineering , no. : 547-553.

Conference paper
Published: 05 January 2021 in Lecture Notes in Electrical Engineering
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With the advent of malware generation toolkits that automatically generate malware, anyone without a professional skill can easily generate malware. As a result, the number of new/modified malware samples is rapidly increasing. The malware generated in this way attacks vulnerabilities, such as PCs and mobile devices without security patch, causing damages involving malicious actions, such as personal information leakage, theft of authorized certificates, and cryptocurrency mining. To solve this problem, most security companies use the signature-based malware detection technique to detect malware, in which the signatures of known malware and files suspected to be malware are compared before detecting malware. However, the signature-based malware detection technique has a limitation in that it is not efficient for detecting new/modified malware which is generated rapidly. Recently, research is underway to utilize deep learning technology for detecting new/modified malware. In this study, we propose a SAT scheme that can detect not only known malware but also new/modified malware more quickly and accurately, thereby reducing malware-induced damages to PCs and mobile devices. The SAT scheme employs an open source library called Tensorflow in the GPU environment to learn malware signatures and then to statically analyze malware.

ACS Style

Jueun Jeon; Juho Kim; Sunyong Jeon; Sungmin Lee; Young-Sik Jeong. Static Analysis for Malware Detection with Tensorflow and GPU. Lecture Notes in Electrical Engineering 2021, 537 -546.

AMA Style

Jueun Jeon, Juho Kim, Sunyong Jeon, Sungmin Lee, Young-Sik Jeong. Static Analysis for Malware Detection with Tensorflow and GPU. Lecture Notes in Electrical Engineering. 2021; ():537-546.

Chicago/Turabian Style

Jueun Jeon; Juho Kim; Sunyong Jeon; Sungmin Lee; Young-Sik Jeong. 2021. "Static Analysis for Malware Detection with Tensorflow and GPU." Lecture Notes in Electrical Engineering , no. : 537-546.

Journal article
Published: 20 May 2020 in IEEE Access
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Internet of Things (IoT) technology provides the basic infrastructure for a hyper connected society where all things are connected and exchange information through the Internet. IoT technology is fused with 5G and artificial intelligence (AI) technologies for use various fields such as the smart city and smart factory. As the demand for IoT technology increases, security threats against IoT infrastructure, applications, and devices have also increased. A variety of studies have been conducted on the detection of IoT malware to avoid the threats posed by malicious code. While existing models may accurately detect malicious IoT code identified through static analysis, detecting the new and variant IoT malware quickly being generated may become challenging. This paper proposes a dynamic analysis for IoT malware detection (DAIMD) to reduce damage to IoT devices by detecting both well-known IoT malware and new and variant IoT malware evolved intelligently. The DAIMD scheme learns IoT malware using the convolution neural network (CNN) model and analyzes IoT malware dynamically in nested cloud environment. DAIMD performs dynamic analysis on IoT malware in a nested cloud environment to extract behaviors related to memory, network, virtual file system, process, and system call. By converting the extracted and analyzed behavior data into images, the behavior images of IoT malware are classified and trained in the Convolution Neural Network (CNN). DAIMD can minimize the infection damage of IoT devices from malware by visualizing and learning the vast amount of behavior data generated through dynamic analysis.

ACS Style

Jueun Jeon; Jong Hyuk Park; Young-Sik Jeong. Dynamic Analysis for IoT Malware Detection With Convolution Neural Network Model. IEEE Access 2020, 8, 96899 -96911.

AMA Style

Jueun Jeon, Jong Hyuk Park, Young-Sik Jeong. Dynamic Analysis for IoT Malware Detection With Convolution Neural Network Model. IEEE Access. 2020; 8 (99):96899-96911.

Chicago/Turabian Style

Jueun Jeon; Jong Hyuk Park; Young-Sik Jeong. 2020. "Dynamic Analysis for IoT Malware Detection With Convolution Neural Network Model." IEEE Access 8, no. 99: 96899-96911.

Journal article
Published: 04 February 2020 in Computer Communications
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Recently, Internet of Drones (IoD) are issued to utilize the diverse kinds of drones for leisure, education and so on. Researchers study to prevent the situations that drones are disabled by cyber-attackers by embedding malwares into the drones and Ground Control Stations (GCS). Therefore, it is required to protect the malwares considering the diverse kinds of features of the drones and GCSs. Signature-based detection approaches are traditionally utilized. However, given that those approaches only scan files partially, some of malwares are not detected. This paper proposes a novel method for finding the malwares in GCSs that utilizes a fastText model to create lower-dimension vectors than those the vectors by one-hot encoding and a bidirectional LSTM model to analyze the correlation with sequential opcodes. In addition, API function names are utilized to increase the classification accuracy of the sequential opcodes. In the experiments, the Microsoft malware classification challenge dataset was utilized and the malwares in the dataset were classified by family types. The proposed method showed the performance improvement of 1.87% comparing with the performance by a one-hot encoding-based approach. When the proposed method was compared with a similar decision tree-based malware detection approach, the performance of the proposed method was improved by 0.76%.

ACS Style

Yunsick Sung; SeJun Jang; Young-Sik Jeong; Jong Hyuk (James J.) Park. Malware classification algorithm using advanced Word2vec-based Bi-LSTM for ground control stations. Computer Communications 2020, 153, 342 -348.

AMA Style

Yunsick Sung, SeJun Jang, Young-Sik Jeong, Jong Hyuk (James J.) Park. Malware classification algorithm using advanced Word2vec-based Bi-LSTM for ground control stations. Computer Communications. 2020; 153 ():342-348.

Chicago/Turabian Style

Yunsick Sung; SeJun Jang; Young-Sik Jeong; Jong Hyuk (James J.) Park. 2020. "Malware classification algorithm using advanced Word2vec-based Bi-LSTM for ground control stations." Computer Communications 153, no. : 342-348.

Conference paper
Published: 04 December 2019 in Lecture Notes in Electrical Engineering
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A range of studies regarding the development of basic technologies for IoT in buildings are under way because the use of cloud services with this type of system is being more widely applied. If the IoT environment in a building is established using an existing cloud service, the client has to receive data from the cloud through a wide area network (WAN). Focusing on the fact that sensor data can be received directly from the hub through a local area network (LAN), this study proposes a method applying a WAN and LAN in parallel. This new approach is expected to increase the data reception speed on the client’s end in the IoT environment found in buildings.

ACS Style

HwiRim Byun; Hyeyoung Kang; Hyun-Woo Kim; Young-Sik Jeong. Effective Data Transfer Method Using Local Network in Building IoT Environments. Lecture Notes in Electrical Engineering 2019, 369 -375.

AMA Style

HwiRim Byun, Hyeyoung Kang, Hyun-Woo Kim, Young-Sik Jeong. Effective Data Transfer Method Using Local Network in Building IoT Environments. Lecture Notes in Electrical Engineering. 2019; ():369-375.

Chicago/Turabian Style

HwiRim Byun; Hyeyoung Kang; Hyun-Woo Kim; Young-Sik Jeong. 2019. "Effective Data Transfer Method Using Local Network in Building IoT Environments." Lecture Notes in Electrical Engineering , no. : 369-375.

Journal article
Published: 15 October 2019 in Applied Sciences
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Cloud computing services that provide computing resources to users through the Internet also provide computing resources in a virtual machine form based on virtualization techniques. In general, supercomputing and grid computing have mainly been used to process large-scale jobs occurring in scientific, technical, and engineering application domains. However, services that process large-scale jobs in parallel using idle virtual machines are not provided in cloud computing at present. Generally, users do not use virtual machines anymore, or they do not use them for a long period of time, because existing cloud computing assigns all of the use rights of virtual machines to users, resulting in the low use of computing resources. This study proposes a scheme to process large-scale jobs in parallel, using idle virtual machines and increasing the resource utilization of idle virtual machines. Idle virtual machines are basically identified through specific determination criteria out of virtual machines created using OpenStack, and then they are used in computing services. This is called the idle virtual machine–resource utilization (IVM–ReU), which is proposed in this study.

ACS Style

Jueun Jeon; Jong Hyuk Park; Young-Sik Jeong. Resource Utilization Scheme of Idle Virtual Machines for Multiple Large-Scale Jobs Based on OpenStack. Applied Sciences 2019, 9, 4327 .

AMA Style

Jueun Jeon, Jong Hyuk Park, Young-Sik Jeong. Resource Utilization Scheme of Idle Virtual Machines for Multiple Large-Scale Jobs Based on OpenStack. Applied Sciences. 2019; 9 (20):4327.

Chicago/Turabian Style

Jueun Jeon; Jong Hyuk Park; Young-Sik Jeong. 2019. "Resource Utilization Scheme of Idle Virtual Machines for Multiple Large-Scale Jobs Based on OpenStack." Applied Sciences 9, no. 20: 4327.

Conference paper
Published: 22 August 2019 in Lecture Notes in Electrical Engineering
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Virtual machine provides cloud computing services that offer computing resources to users through the Internet based on virtualization technology. Generally, supercomputing or grid computing has been used to process a large scale job in scientific, technology, and engineering application problems. Currently, services for large scale parallel processing through idle virtual machines in cloud computing are not provided. Previously, the utilization rate of computing resources in cloud computing was low when users do not use virtual machines anymore or for a long period of time since all the rights in relation to the use of virtual machine are given to users. This study proposes a scheme that increase resource utilization of idle virtual machines and process a large scale job through the idle virtual machines. Basically, idle virtual machines are identified based on virtual machines created through OpenStack, and idle virtual machine-computing service (IVM-CS) is proposed.

ACS Style

Jueun Jeon; Seungchul Kim; Gisung Yu; Hyun-Woo Kim; Young-Sik Jeong. Computing Service Scheme with Idle Virtual Machine Based on OpenStack. Lecture Notes in Electrical Engineering 2019, 207 -212.

AMA Style

Jueun Jeon, Seungchul Kim, Gisung Yu, Hyun-Woo Kim, Young-Sik Jeong. Computing Service Scheme with Idle Virtual Machine Based on OpenStack. Lecture Notes in Electrical Engineering. 2019; ():207-212.

Chicago/Turabian Style

Jueun Jeon; Seungchul Kim; Gisung Yu; Hyun-Woo Kim; Young-Sik Jeong. 2019. "Computing Service Scheme with Idle Virtual Machine Based on OpenStack." Lecture Notes in Electrical Engineering , no. : 207-212.

Journal article
Published: 28 June 2019 in Computers & Electrical Engineering
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Signature-based malware detection approaches are inadequate for detecting the increasingly intelligent and large number of malware programs emerging today. Therefore, alternative approaches are required. The effects of malware can be estimated by analyzing the opcodes in its executable files. It can then be classified into families using a long short-term memory (LSTM) network. Vectorizing opcodes and application programming interface (API) function names using one-hot encoding results in high-dimensional vectors because each case is represented using one dimension. Therefore, this paper proposes a word2vec-based LSTM method to analyze opcodes and API function names using fewer dimensions. The results of opcode and API function name classification using the proposed method and one-hot encoding were compared using the Microsoft Malware Classification Challenge dataset. The proposed method showed approximately 0.5% higher performance than the one-hot encoding-based approach.

ACS Style

Jungho Kang; SeJun Jang; Shuyu Li; Young-Sik Jeong; Yunsick Sung. Long short-term memory-based Malware classification method for information security. Computers & Electrical Engineering 2019, 77, 366 -375.

AMA Style

Jungho Kang, SeJun Jang, Shuyu Li, Young-Sik Jeong, Yunsick Sung. Long short-term memory-based Malware classification method for information security. Computers & Electrical Engineering. 2019; 77 ():366-375.

Chicago/Turabian Style

Jungho Kang; SeJun Jang; Shuyu Li; Young-Sik Jeong; Yunsick Sung. 2019. "Long short-term memory-based Malware classification method for information security." Computers & Electrical Engineering 77, no. : 366-375.

Article
Published: 15 May 2019 in The Journal of Supercomputing
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ACS Style

Young-Sik Jeong; Houcine Hassan; Arun Kumar Sangaiah. Machine learning on big data for future computing. The Journal of Supercomputing 2019, 75, 2925 -2929.

AMA Style

Young-Sik Jeong, Houcine Hassan, Arun Kumar Sangaiah. Machine learning on big data for future computing. The Journal of Supercomputing. 2019; 75 (6):2925-2929.

Chicago/Turabian Style

Young-Sik Jeong; Houcine Hassan; Arun Kumar Sangaiah. 2019. "Machine learning on big data for future computing." The Journal of Supercomputing 75, no. 6: 2925-2929.

Article
Published: 10 May 2019 in The Journal of Supercomputing
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In recent years, as IT technology has progressed, mobile devices have been created that enable various manual tasks to be automated and portable. A variety of mobile devices has computing, storage, and Internet capabilities and can handle many tasks. When miniaturized mobile devices perform tasks that require a large amount of computing resources due to limited computing and storage, there is a delay in operation and a non-operation state. Therefore, collaborative-based mobile cloud infrastructure (MCI) research is being conducted to provide computing services composed of mobile devices. Computation off-loading studies have been conducted for MCI’s high-performance computing, but it is difficult to build various mobile infrastructures and verify algorithm performance. In addition, performance verification is performed in a predetermined MCI environment or is carried out through small-scale test equipment. This causes waste of time, cost, and manpower for constructing the environment. Various studies have been conducted for this purpose, but there is a difficulty in performance verification and analysis since only the results are displayed or outputted in text form. In this paper, we propose a mobile cloud infrastructure simulator (MCIS) for computing off-loading, resource management, mobile deployment, and mobile information for MCI. MCIS enables user tasks, resource allocation methods, and various mobile device performance settings. In addition, visualization of the operating state makes it easy to analyze the performance of the user, and it is possible to grasp the problems that occur during operation.

ACS Style

Hyun-Woo Kim; Jungho Kang; Young-Sik Jeong. Simulator considering modeling and performance evaluation for high-performance computing of collaborative-based mobile cloud infrastructure. The Journal of Supercomputing 2019, 75, 4459 -4471.

AMA Style

Hyun-Woo Kim, Jungho Kang, Young-Sik Jeong. Simulator considering modeling and performance evaluation for high-performance computing of collaborative-based mobile cloud infrastructure. The Journal of Supercomputing. 2019; 75 (8):4459-4471.

Chicago/Turabian Style

Hyun-Woo Kim; Jungho Kang; Young-Sik Jeong. 2019. "Simulator considering modeling and performance evaluation for high-performance computing of collaborative-based mobile cloud infrastructure." The Journal of Supercomputing 75, no. 8: 4459-4471.

Original research
Published: 01 April 2019 in Journal of Ambient Intelligence and Humanized Computing
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Recently, intra-cloud research has been actively conducted to reduce the waste of idle resources in distributed desktops and to increase resource utilization. Intra-cloud integrates the idle resources of distributed desktops to provide computing and storage services to users. Existing intra-cloud have only studied storage of large files and simple computing services. Research is needed for computing services of multimedia field such as video and audio in the intra-cloud. This paper proposes a diversify scheme for multiform video resources (DSMVR), which is a video transcoding scheme of multimedia data-hiding based on the parallel computing framework and the intra-cloud environment, in order to transcode for multiform resource types within the intra-cloud, which composed to computing infrastructure using legacy desktops. Its target users are community user groups within a certain size. By using a small-scale server group, parallel processing framework and improved task assignment algorithm, high-speed video transcoding can be realized by using ffmpeg, which is a vast software suite of libraries and programs designed for handling video, audio, and other multimedia files and streams, and different-definition videos are generated step by step at high speed. By using the DSMVR scheme, the size of a task can be dynamically analyzed in order to select the number of task processing servers required, thus ensuring the high scalability of the DSMVR. Thanks to these operations, the user can smoothly play videos at resolutions that are suitable for different smart devices.

ACS Style

Hyun-Woo Kim; He Mu; Jong Hyuk Park; Arun Kumar Sangaiah; Young-Sik Jeong. Video transcoding scheme of multimedia data-hiding for multiform resources based on intra-cloud. Journal of Ambient Intelligence and Humanized Computing 2019, 11, 1809 -1819.

AMA Style

Hyun-Woo Kim, He Mu, Jong Hyuk Park, Arun Kumar Sangaiah, Young-Sik Jeong. Video transcoding scheme of multimedia data-hiding for multiform resources based on intra-cloud. Journal of Ambient Intelligence and Humanized Computing. 2019; 11 (5):1809-1819.

Chicago/Turabian Style

Hyun-Woo Kim; He Mu; Jong Hyuk Park; Arun Kumar Sangaiah; Young-Sik Jeong. 2019. "Video transcoding scheme of multimedia data-hiding for multiform resources based on intra-cloud." Journal of Ambient Intelligence and Humanized Computing 11, no. 5: 1809-1819.

Journal article
Published: 08 March 2019 in Future Generation Computer Systems
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Recently, with the rapid growth of information technology (IT), diverse studies have been carried out for the grafting of devices based on the Internet of Things (IoT) for use in real life. With certain sensor functions and downsized mobile devices, IoT devices have improved users’ work efficiency, ease of mobility, and convenience in terms of not being restricted by location. In the case of IoT devices as such, computing offloading is regarded to be very important to overcome issues of limited computing power and storage capacity and the limitations of built-in batteries. For the computing offloading of IoT devices, diverse job allocation techniques considering performance resources have been studied. However, since only the static performance, dynamic performance, or performance and battery size of IoT devices are considered in job allocation, job reallocation problems are caused by battery consumption due to the use of patterns in which users execute certain applications. In this paper, an adaptive job allocation scheduler (AJAS) that adaptively redistributes the jobs allocated to IoT devices based on user behavior patterns is proposed. The AJAS allocates jobs using the dynamic performance resources and battery consumption rates of diverse IoT devices. In addition, the AJAS measures the battery consumption rate of user applications executed in the IoT device to assess whether the allocated jobs can be processed. The AJAS identifies IoT devices that cannot process jobs and minimizes states in which allocated jobs cannot be processed due to battery exhaustion and delay time due to job reallocation. For verification, an AJAS is designed and implemented to show that the AJAS improves device availability for job processing.

ACS Style

Hyun-Woo Kim; Jong Hyuk Park; Young-Sik Jeong. Adaptive job allocation scheduler based on usage pattern for computing offloading of IoT. Future Generation Computer Systems 2019, 98, 18 -24.

AMA Style

Hyun-Woo Kim, Jong Hyuk Park, Young-Sik Jeong. Adaptive job allocation scheduler based on usage pattern for computing offloading of IoT. Future Generation Computer Systems. 2019; 98 ():18-24.

Chicago/Turabian Style

Hyun-Woo Kim; Jong Hyuk Park; Young-Sik Jeong. 2019. "Adaptive job allocation scheduler based on usage pattern for computing offloading of IoT." Future Generation Computer Systems 98, no. : 18-24.

Article
Published: 07 February 2019 in The Journal of Supercomputing
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In recent years, various studies on OpenStack-based high-performance computing have been conducted. OpenStack combines off-the-shelf physical computing devices and creates a resource pool of logical computing. The configuration of the logical computing resource pool provides computing infrastructure according to the user’s request and can be applied to the infrastructure as a service (laaS), which is a cloud computing service model. The OpenStack-based cloud computing can provide various computing services for users using a virtual machine (VM). However, intensive computing service requests from a large number of users during large-scale computing jobs may delay the job execution. Moreover, idle VM resources may occur and computing resources are wasted if users do not employ the cloud computing resources. To resolve the computing job delay and waste of computing resources, a variety of studies are required including computing task allocation, job scheduling, utilization of idle VM resource, and improvements in overall job’s execution speed according to the increase in computing service requests. Thus, this paper proposes an efficient job management of computing service (EJM-CS) by which idle VM resources are utilized in OpenStack and user’s computing services are processed in a distributed manner. EJM-CS logically integrates idle VM resources, which have different performances, for computing services. EJM-CS improves resource wastes by utilizing idle VM resources. EJM-CS takes multiple computing services rather than single computing service into consideration. EJM-CS determines the job execution order considering workloads and waiting time according to job priority of computing service requester and computing service type, thereby providing improved performance of overall job execution when computing service requests increase.

ACS Style

Seok-Hyeon Han; Hyun-Woo Kim; Young-Sik Jeong. An efficient job management of computing service using integrated idle VM resources for high-performance computing based on OpenStack. The Journal of Supercomputing 2019, 75, 4388 -4407.

AMA Style

Seok-Hyeon Han, Hyun-Woo Kim, Young-Sik Jeong. An efficient job management of computing service using integrated idle VM resources for high-performance computing based on OpenStack. The Journal of Supercomputing. 2019; 75 (8):4388-4407.

Chicago/Turabian Style

Seok-Hyeon Han; Hyun-Woo Kim; Young-Sik Jeong. 2019. "An efficient job management of computing service using integrated idle VM resources for high-performance computing based on OpenStack." The Journal of Supercomputing 75, no. 8: 4388-4407.

Conference paper
Published: 29 November 2018 in Lecture Notes in Electrical Engineering
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STVF (Novel scheme for transcoding video file of multiple file formats) is a video sharing system novel scheme based on the parallel computing framework and Intra-cloud environment. The target user is community user within a certain scale. While using a small-scale server group, a parallel processing framework and an improved task assignment algorithm are utilized to realize high-speed video transcoding using ffmpeg, and different-definition videos are generated at high speed too. Dynamically analyze the size of the task to select the number of task processing servers to achieve STVF’s higher scalability. And through these operations so that the user can smoothly play suitable resolution in different smart machines format.

ACS Style

Seungchul Kim; Mu He; Hyun-Woo Kim; Young-Sik Jeong. Rapid Parallel Transcoding Scheme for Providing Multiple-Format of a Single Multimedia. Lecture Notes in Electrical Engineering 2018, 855 -861.

AMA Style

Seungchul Kim, Mu He, Hyun-Woo Kim, Young-Sik Jeong. Rapid Parallel Transcoding Scheme for Providing Multiple-Format of a Single Multimedia. Lecture Notes in Electrical Engineering. 2018; ():855-861.

Chicago/Turabian Style

Seungchul Kim; Mu He; Hyun-Woo Kim; Young-Sik Jeong. 2018. "Rapid Parallel Transcoding Scheme for Providing Multiple-Format of a Single Multimedia." Lecture Notes in Electrical Engineering , no. : 855-861.

Editorial
Published: 25 October 2018 in Sustainable Computing: Informatics and Systems
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ACS Style

Arun Kumar Sangaiah; Christian Esposito; Zhigao Zheng; Young-Sik Jeong. Introduction to special issue on sustainable computing for bio-energy: Intelligent computing models and analytics. Sustainable Computing: Informatics and Systems 2018, 20, 118 -119.

AMA Style

Arun Kumar Sangaiah, Christian Esposito, Zhigao Zheng, Young-Sik Jeong. Introduction to special issue on sustainable computing for bio-energy: Intelligent computing models and analytics. Sustainable Computing: Informatics and Systems. 2018; 20 ():118-119.

Chicago/Turabian Style

Arun Kumar Sangaiah; Christian Esposito; Zhigao Zheng; Young-Sik Jeong. 2018. "Introduction to special issue on sustainable computing for bio-energy: Intelligent computing models and analytics." Sustainable Computing: Informatics and Systems 20, no. : 118-119.

Journal article
Published: 17 July 2018 in Computers in Human Behavior
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– In the recent year, with the emergence of various smart devices, the data is explosively increasing in the social Internet of Things (IoT) such as human healthcare. These data mainly involve information about user behaviors collected from various heterogeneous wireless sensor and social networks. Therefore, it is vital to analyze the data to find hidden meaning and convert it into valuable information. Due to the lack of capability to handle a wide range of queries, a traditional relational database provides inefficient analysis for the data. A graph database can easily store and analyze the data from various heterogeneous wireless sensor and social network using team formation algorithm. In the healthcare field, it is important to form a team that manages patients' health efficiently. The final goal of team formation is to organize experts who can perform task of data analysis. However, the existing team formation algorithms rely on a centralized computing environment and require high communication cost among experts to form a team. In this paper, we propose a parallel team formation method on apache spark (PTFS) to analyze graph data considering the crowd intelligence capability that exists in the graph data and social network. The PTFS employs two computation stages - a find skill and a merger subgraph and provides the parallel execution of many map tasks of graph data analysis. The experimental evaluation of the proposed method on a graph dataset demonstrates that it minimizes the communicating cost of the team members to form an optimized expert team in which a desired skill set is assigned to accomplish the graph data analysis.

ACS Style

Young-Sik Jeong; Yi Pan; Shailendra Rathore; Byoungwook Kim; Jong Hyuk Park. A parallel team formation approach using crowd intelligence from social network. Computers in Human Behavior 2018, 101, 429 -434.

AMA Style

Young-Sik Jeong, Yi Pan, Shailendra Rathore, Byoungwook Kim, Jong Hyuk Park. A parallel team formation approach using crowd intelligence from social network. Computers in Human Behavior. 2018; 101 ():429-434.

Chicago/Turabian Style

Young-Sik Jeong; Yi Pan; Shailendra Rathore; Byoungwook Kim; Jong Hyuk Park. 2018. "A parallel team formation approach using crowd intelligence from social network." Computers in Human Behavior 101, no. : 429-434.

Journal article
Published: 11 May 2018 in Human-centric Computing and Information Sciences
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The recent advances in information technology for mobile devices have increased the work efficiency of users, the mobility of compact mobile devices, and the convenience of location independence. However, mobile devices have limited computing power and storage capacity, so mobile cloud computing is being researched to overcome these limitations in mobile devices. Mobile cloud computing is divided into two methods: the use of external cloud services and the use of mobile resource management without a cloud server (MRM), which integrates the computing and storage resources of nearby mobile devices. Because mobile devices can freely participate in MRM, it is critical to have authentication technology to determine the correctness of information regarding resources. Conventional technologies require strong authentication techniques because they have vulnerabilities that can easily be tampered with via man-in-the-middle (MITM) attacks. This paper proposes the Secure Authentication Management human-centric Scheme (SAMS) to authenticate mobile devices using blockchain for trusting resource information in the mobile devices that are participating in the MRM resource pool. The SAMS forms a blockchain based on the resource information of the subordinate client nodes around the master node in the MRM. Devices in the MRM that have not been authorized through the SAMS cannot access or falsify data. To verify the SAMS for application with MRM, it was tested for data falsification by a malicious user accessing the SAMS, and the results show that data falsification is impossible.

ACS Style

Hyun-Woo Kim; Young-Sik Jeong. Secure Authentication-Management human-centric Scheme for trusting personal resource information on mobile cloud computing with blockchain. Human-centric Computing and Information Sciences 2018, 8, 11 .

AMA Style

Hyun-Woo Kim, Young-Sik Jeong. Secure Authentication-Management human-centric Scheme for trusting personal resource information on mobile cloud computing with blockchain. Human-centric Computing and Information Sciences. 2018; 8 (1):11.

Chicago/Turabian Style

Hyun-Woo Kim; Young-Sik Jeong. 2018. "Secure Authentication-Management human-centric Scheme for trusting personal resource information on mobile cloud computing with blockchain." Human-centric Computing and Information Sciences 8, no. 1: 11.

Journal article
Published: 29 March 2018 in Bioinformatics
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SummaryNext-generation sequencing (NGS) technologies have led to the accumulation of high-throughput sequence data from various organisms in biology. To apply gene annotation of organellar genomes for various organisms, more optimized tools for functional gene annotation are required. Almost all gene annotation tools are mainly focused on the chloroplast genome of land plants or the mitochondrial genome of animals. We have developed a web application AGORA for the fast, user-friendly and improved annotations of organellar genomes. Annotator for Genes of Organelle from the Reference sequence Analysis (AGORA) annotates genes based on a basic local alignment search tool (BLAST)-based homology search and clustering with selected reference sequences from the NCBI database or user-defined uploaded data. AGORA can annotate the functional genes in almost all mitochondrion and plastid genomes of eukaryotes. The gene annotation of a genome with an exon–intron structure within a gene or inverted repeat region is also available. It provides information of start and end positions of each gene, BLAST results compared with the reference sequence and visualization of gene map by OGDRAW.Availability and implementationUsers can freely use the software, and the accessible URL is https://bigdata.dongguk.edu/gene_project/AGORA/. The main module of the tool is implemented by the python and php, and the web page is built by the HTML and CSS to support all browsers.Supplementary informationSupplementary data are available at Bioinformatics online.

ACS Style

Jaehee Jung; Jong Im Kim; Young-Sik Jeong; Gangman Yi. AGORA: organellar genome annotation from the amino acid and nucleotide references. Bioinformatics 2018, 34, 2661 -2663.

AMA Style

Jaehee Jung, Jong Im Kim, Young-Sik Jeong, Gangman Yi. AGORA: organellar genome annotation from the amino acid and nucleotide references. Bioinformatics. 2018; 34 (15):2661-2663.

Chicago/Turabian Style

Jaehee Jung; Jong Im Kim; Young-Sik Jeong; Gangman Yi. 2018. "AGORA: organellar genome annotation from the amino acid and nucleotide references." Bioinformatics 34, no. 15: 2661-2663.

Journal article
Published: 08 February 2018 in IEEE Access
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The rapid development of ICT has led to the wide popularity of mobile devices, which have helped improve business efficiency and enabled simple mobility as small and light devices and convenience of being available anytime, anywhere for cyber-physical-social big data. There are many ongoing studies on mobile cloud computing (MCC) to overcome the limited computing capability and storage capacity and internal battery limitation by taking advantage of the popularity of mobile devices for the processing cyber-physical-social big data. MCC consists of service-oriented architecture, agent-client architecture, and collaborative architecture, with job splitting and allocation as the critical factor. As such, job allocation techniques considering the performance resources of mobile devices have been studied. Note, however, that there is a problem of job reallocation due to continuous battery consumption since the studies consider only the performance resources of mobile devices at the time of job allocation or take into account the performance resources and remaining battery power only. This paper proposes the job allocation mechanism (JAM) for battery consumption minimization of cyber-physical-social big data processing in MCC, which continuously reflects the battery consumption rate to process jobs with mobile devices only without an external cloud server in a collaborative architecture-based MCC environment. JAM allocates jobs considering the periodic measurement of battery consumption and surplus resource to minimize the problem of job reallocation due to battery rundown of the mobile devices. This research designs and implements a system for verifying JAM and demonstrated that the job processing speed increased in an MCC environment for cyber-physical-social big data.

ACS Style

Gangman Yi; Hyun-Woo Kim; Jong Hyuk Park; Young-Sik Jeong. Job Allocation Mechanism for Battery Consumption Minimization of Cyber-Physical-Social Big Data Processing Based on Mobile Cloud Computing. IEEE Access 2018, 6, 21769 -21777.

AMA Style

Gangman Yi, Hyun-Woo Kim, Jong Hyuk Park, Young-Sik Jeong. Job Allocation Mechanism for Battery Consumption Minimization of Cyber-Physical-Social Big Data Processing Based on Mobile Cloud Computing. IEEE Access. 2018; 6 ():21769-21777.

Chicago/Turabian Style

Gangman Yi; Hyun-Woo Kim; Jong Hyuk Park; Young-Sik Jeong. 2018. "Job Allocation Mechanism for Battery Consumption Minimization of Cyber-Physical-Social Big Data Processing Based on Mobile Cloud Computing." IEEE Access 6, no. : 21769-21777.

Journal article
Published: 29 January 2018 in IEEE Internet of Things Journal
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Technological advances over the last decade in the field of wireless communications have resulted in the improvement of small and low cost sensor nodes outfitted with wireless communication abilities capable of establishing Wireless Sensor Network (WSN). Due to the expansion of Internet of Things (IoT), there are many areas in IoT application where WSN applications are found. These applications generally impose severe constraints on the lifetime of the WSN, which is expected to last several years. It is necessary to diminish the overall energy consumption of the sensor node and to find an additional source of energy for achieving this objective. On the other hand, due to the imminent crisis of the Radio Frequency (RF) spectrum, Light Fidelity (LiFi) offers many key benefits and effective solutions for these issues that have been postured in the most recent decade. In this paper, we propose a novel EH-HL model for future smart homes and industries based on the integration of Energy Harvesting Wireless Sensor Network (EH-WSNs) and hybrid LiFi/WiFi communication techniques. The proposed model is capable of efficiently transmitting data at high speed for bidirectional multi-device and by harvesting energy, we provide the power to the sensor nodes. To synchronize multi-device transmissions, transmit data and provide low-cost wireless communication, we used the color beams of the RGB LEDs. The result of the evaluation shows that the hybrid communication scheme is proposed in the EH-HL model. It also offers superior performance and achieves a data rate of 25 Mbps for multi-access/multi-users.

ACS Style

Pradip Kumar Sharma; Young-Sik Jeong; Jong Hyuk Park. EH-HL: Effective Communication Model by Integrated EH-WSN and Hybrid LiFi/WiFi for IoT. IEEE Internet of Things Journal 2018, 5, 1719 -1726.

AMA Style

Pradip Kumar Sharma, Young-Sik Jeong, Jong Hyuk Park. EH-HL: Effective Communication Model by Integrated EH-WSN and Hybrid LiFi/WiFi for IoT. IEEE Internet of Things Journal. 2018; 5 (3):1719-1726.

Chicago/Turabian Style

Pradip Kumar Sharma; Young-Sik Jeong; Jong Hyuk Park. 2018. "EH-HL: Effective Communication Model by Integrated EH-WSN and Hybrid LiFi/WiFi for IoT." IEEE Internet of Things Journal 5, no. 3: 1719-1726.