• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.405-413
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• 2016

In this paper, we propose a proactive inference method of finding suspicious domains. Our method detects potential malicious domains from the seed domain information extracted from the TLD Zone files and WHOIS information. The inference process follows the three steps: searching the candidate domains, machine learning, and generating a suspicious domain pool. In the first step, we search the TLD Zone files and build a candidate domain set which has the same name server information with the seed domain. The next step clusters the candidate domains by the similarity of the WHOIS information. The final step in the inference process finds the seed domain's cluster, and make the cluster as a suspicious domain set. In experiments, we used .COM and .NET TLD Zone files, and tested 10 seed domains selected by our analysts. The experimental results show that our proposed method finds 55 suspicious domains and 52 true positives. F1 scores 0.91, and precision is 0.95 We hope our proposal will contribute to the further proactive malicious domain blacklisting research.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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• pp.1407-1411
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• 2005

Tn these server oriented network forms, users allowed to approach the a certain server only in slave and passive way, and are limited in sharing informations, and internet choose from a method of traditional centralized web server until now, so we pay a lot of money and have a problem of maintenance management. The P2P is the new solution of these limitations, but there is no verification about the possibility and validity of p2p as the business model. This parer offering to some solution for those problems using the JXTA technique and P2P compution. We will apply the JXTA platform that wireless environment as mobile environment. The JXTA technique can be interoperability between third-party. This paper is implementation of P2P communication system on mobile environment. It's implementation used by JXTA technique, J2ME and XML.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.2
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• pp.77-82
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• 2015

Big Data technology has been attracted much attention in aspect of fast data processing. Research of practicing Big Data technology is also ongoing to process large-scale structured data much faster in Relatioinal Database(RDB). Although there are lots of studies about measuring analyzing performance, studies about structured data loading performance, prior step of analyzing, is very rare. Thus, in this study, structured data in RDB is tested the performance that loads distributed processing platform Hadoop using Apache sqoop. Also in order to analyze the influence factors of data loading, it is tested repeatedly with different options of data loading and compared with data loading performance among RDB based servers. Although data loading performance of Apache Sqoop in test environment was low, but in large-scale Hadoop cluster environment we can expect much better performance because of getting more hardware resources. It is expected to be based on study improving data loading performance and whole steps of performance analyzing structured data in Hadoop Platform.

• Journal of the Korea Society for Simulation
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• v.21 no.4
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• pp.47-56
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• 2012

In this study, we use a parallel simulator PASENS(Parallel SEnsor Network Simulator) to predict power consumption and data reception rate of the hierarchical routing protocols for sensor network - LEACH (Low-Energy Adaptive Clustering Hierarchy), TL-LEACH (Two Level Low-Energy Adaptive Clustering Hierarchy), M-LEACH (Multi hop Low-Energy Adaptive Clustering Hierarchy) and LEACH-C (LEACH-Centralized). According to simulation results, M-LEACH routing protocol shows the highest data reception rate for the wider area, since more sensor nodes are involved in the data transmission. And LEACH-C routing protocol, where the sink node considers the entire node's residual energy and location to determine the cluster head, results in the most efficient energy consumption and in the narrow area needed long life of sensor network.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.152-161
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• 2014

In this paper, a multi-node real-time diagnostic and management system based on zigbee sensor network is proposed, which is to monitor and diagnose multiple nodes as well as to control the data generated from the various multiple sensors collectively. The proposed system is designed to transmit the collected wireless and wired data to the server for monitoring and controling efficiently the condition for multi-nodes by taking the corresponding actions according to the analysis. The system is implemented to make it possible to manage the sensor data by classifying them, of which data are issued from the clustered sources with a number of the remote sensors. In order to evaluate the performance of the proposed system, we measure and analyze both the transmission delay time according to the distance and the data loss rate issued from multiple sensors. The results shows that the proposed system has a good performance.

• KIISE Transactions on Computing Practices
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• v.22 no.12
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• pp.663-674
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• 2016

Traditional supercomputing services suffer from limited accessibility and low utilization in that users(researchers) may perform computational executions only using terminal-based command line interfaces. To address this problem, in this paper, we provide the design and implementation details of National supercomputing service framework. The proposed framework supports all the fundamental primitive functions such as user management/authentication, heterogeneous computing resource management, HPC (High Performance Computing) job management, etc. so that it enables various 3rd-party applications to be newly built on top of the proposed framework. Our framework also provides Web-based RESTful OpenAPIs and the abstraction interfaces of job schedulers (as well as bundle scheduler plug-ins, for example, LoadLeveler, Open Grid Scheduler, TORQUE) in order to easily integrate the broad spectrum of heterogeneous computing clusters. To show and validate the effectiveness of the proposed framework, we describe the best practice scenario of high energy physics Lattice-QCD as an example application.

• Smart Media Journal
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• v.5 no.4
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• pp.18-25
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• 2016

Virtualized hardware resources provides efficiency in use and easy of management. Based on the benefits, virtualization techniques are used to build large server clusters and cloud systems. The performance of a virtualized system is significantly affected by the virtual machine scheduler. However, the existing virtual machine scheduler have a problem in that the I/O response is reduced in accordance with the scheduling delay becomes longer. In this paper, we introduce the Loan/Redemption mechanism of a virtual machine scheduler in order to improve the responsiveness to I/O events. The proposed scheme gives additional credits for to virtual machines and classifies the task characteristics of each virtual machine by analyzing the credit consumption pattern. When an I/O event arrives, the scheduling priority of a virtual machine is temporally increased based on the analysis. The evaluation based on the implementation shows that the proposed scheme improves the I/O response 60% and bandwidth of virtual machines 62% compared to those of the existing virtual machine scheduler.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.617-621
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• 2004

In recent, researchers in the field of Bioinformatics need to analyze thousands of genome sequences efficiently according to introduce of new analysis methods and technologies such as genome expression microchip. This rapid growth in the field of bio-engineering needs computing resources to analyze rapidly for genome sequences, but it does not introduce the computing resources due to an enormous investment expense. The core factor of this study is integrated environment based PC-Cluster system & high speed access rate up to 155Mbps, continuous collection system for bio-information at home and abroad. The results of the study are establishment & stabilization of information and communication infrastructure, establishment & stabilization of high performance computer network up to 155Mbps, development of PC-Cluster system with 32 nodes, a parallel BLAST on Cluster system, which can provides scalable speedup in terms of response time, and development of collection & search system for bio-information.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.10
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• pp.231-238
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• 2019

Analyzing next-generation genome sequencing data in a conventional way using single server may take several tens of hours depending on the data size. However, in order to cope with emergency situations where the results need to be known within a few hours, it is required to improve the performance of a single genome analysis. In this paper, we propose a parallelized method for pre-processing genome sequence data which can reduce the analysis time by utilizing the big data technology and the highperformance computing cluster which is connected to the high-speed network and shares the parallel file system. For the reliability of analytical data, we have chosen a strategy to parallelize the existing analytical tools and algorithms to the new environment. Parallelized processing, data distribution, and parallel merging techniques have been developed and performance improvements have been confirmed through experiments.

• KNOM Review
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• v.23 no.1
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• pp.43-50
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• 2020

As General Purpose Graphics Processing Unit (GPGPU) recently plays an essential role in high-performance computing, several cloud service providers offer GPU service. Most cluster orchestration platforms in a cloud environment using containers allocate the integer number of GPU to jobs and do not allow a node shared with other jobs. In this case, resource utilization of a GPU node might be low if a job does not intensively require either many cores or large size of memory in GPU. GPU virtualization brings opportunities to realize kernel concurrency and share resources. However, performance may vary depending on characteristics of applications running concurrently and interference among them due to resource contention on a node. This paper proposes GPU container co-execution framework with multiple server creation and execution based on Kubernetes, container orchestration platform for measuring interference which may be occurred by sharing GPU resources. Performance changes according to scheduling policies were investigated by executing several jobs on GPU. The result shows that optimal scheduling is not possible only considering GPU memory and computing resource usage. Interference caused by co-execution among applications is measured using the framework.