• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6C
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• pp.544-561
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• 2002

In this paper, we propose a new hybrid type of the routing protocol (Virtual Cluster-based Routing Protocol: VCRP) for mobile ad-hoc networks, based on a virtual cluster, which is defined as a narrow-sense network to exchange the basic information related to the routing among the adjacent nodes. This particular approach combines advantage of proactive routing protocol (PRP), which immediately provides the route collecting the network-wide topological and metric information, with that of reactive routing protocol, which relies on the route query packet to collect the route information on its way to the destination without exchanging any information between nodes. Furthermore, it also provides the back-up route as a byproduct, along with the optimal route, which leads to the VCBRP (Virtual Cluster-based Routing Protocol with Backup Route) establishing the alternative route immediately after a network topology is changed due to degradation of link quality and terminal mobility, Our simulation studies have shown that the proposed routing protocols are robust against dynamics of network topology while improving the performances of packet transfer delay, link failure ratio, and throughput over those of the existing routing protocols without much compromising the control overhead efficiency.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.649-658
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• 2005

Since the Network based attack Is extensive in the real state of damage, It is very important to detect intrusion quickly at the beginning. But the intrusion detection using supervised learning needs either the preprocessing enormous data or the manager's analysis. Also it has two difficulties to detect abnormal traffic that the manager's analysis might be incorrect and would miss the real time detection. In this paper, we propose a traffic attributes correlation analysis mechanism based on self-organizing maps(SOM) for the real-time intrusion detection. The proposed mechanism has three steps. First, with unsupervised learning build a map cluster composed of similar traffic. Second, label each map cluster to divide the map into normal traffic and abnormal traffic. In this step there is a rule which is created through the correlation analysis with SOM. At last, the mechanism would the process real-time detecting and updating gradually. During a lot of experiments the proposed mechanism has good performance in real-time intrusion to combine of unsupervised learning and supervised learning than that of supervised learning.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.109-114
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• 2013

In underwater wireless sensor networks, sensing data such temperature and salinity can be merged by averaging them. Data aggregation is a good choice to reduce the amount of packets and save energy in underwater wireless sensor networks. However, data aggregation could bring about packet delay and non-directional transmission to the sink. In this paper, we propose a new path building algorithm based on data aggregation to mitigate these problems. The paper reduces the delay without wireless interferences and maximizes the energy efficiency by removing the non-directional transmission to the sink. Experimental results show that the proposed algorithm outperforms in terms of the energy efficiency and the packet delay.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.91-101
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• 2017

API call sequence analysis is a kind of analysis using API call information extracted in target program. Compared to other techniques, this is advantageous as it can characterize the behavior of the target. However, existing API call sequence analysis has an issue of identifying same characteristics to different function during the analysis. To resolve the identification issue and improve performance of analysis, this study includes the method of API abstraction technique in addition to existing analysis. From there on, similarity between target programs is computed and clustered into similar types by applying LSH to abstracted API call sequence from analyzed target. Thus, this study can attribute in improving the accuracy of the malware analysis based on discovered information on the types of malware identified.

• 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.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.726-730
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• 2009

The proliferation of the web and digital photography has drastically increased multimedia data and has resulted in the need of the high quality internet service based on the moving picture like user generated contents(UGC). The keyword-based search on large scale images and video collections is too expensive and requires much manual intervention. Therefore the web search engine may provide the content-based retrieval on the multimedia data for search accuracy and customer satisfaction. In this paper, we propose a novel distributed index structure based on multiple length signature files according to data distribution. In addition, we describe how our scalable index technique can be used to find the nearest neighbors in the cluster environments.

• Journal of the Economic Geographical Society of Korea
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• v.8 no.1
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• pp.17-29
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• 2005

This study has attempted to analyze the spatial structure of industrial agglomerations with elaborated spatial econometric techniques. First of all, spatial patterns and structures of industrial agglomerations in Korea show a multi-polar spatial pattern of industrial agglomeration, Major industries from industrial agglomerations in the Seoul Metropolitan Area, part of the Chungcheong Area and Dongnam Area. Second, as some industrial agglomerations show an agglomerative pattern beyond a regionally based-administrative jurisdiction, the effects of agglomeration seem to be produced across regionally based-administrative jurisdictions. Finally, it can be considered that industrial agglomerations have generally been produced by spatial divisions of labor in which the functions of conception and execution are separated from each other. According to this results, in designing regional innovation systems, their spatial coverage should draw upon an extended region with a few adjacent provinces, and there is a need to form networked clusters in order to sufficiently capitalize upon the spatial spillovers of agglomerations.

• Convergence Security Journal
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• v.12 no.6
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• pp.25-30
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• 2012

As the network composed of numerous sensor nodes, sensor network conducts the function of sensing the surrounding information by sensor and of the sensed information. Our military has also developed ICT(Information and Communication Technology) along with the methods for effective war by sharing smooth information of battlefield resources through network with each object. In this paper, a sensor network is clustered in advance and a cluster header (CH) is elected for clusters. Before deployment, a certificate is provided between the BS and the sensor nodes, and after clustering, authentication is done between the BS and the sensor nodes. Moreover, inter-CH authentication technique is used to allow active response to destruction or replacement of sensor nodes. Also, because authentication is done twice, higher level of security can be provided.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1136-1146
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• 2005

Fingerprint classification is useful in an automated fingerprint identification system (AFIS) to reduce the matching time by categorizing fingerprints. Based on Henry system that classifies fingerprints into S classes, various techniques such as neural networks and support vector machines (SVMs) have been widely used to classify fingerprints. Especially, SVMs of high classification performance have been actively investigated. Since the SVM is binary classifier, we propose a novel classifier-combination model, multiple decision templates (MuDTs), to classily fingerprints. The method extracts several clusters of different characteristics from samples of a class and constructs a suitable combination model to overcome the restriction of the single model, which may be subject to the ambiguous images. With the experimental results of the proposed on the FingerCodes extracted from NIST Database4 for the five-class and four-class problems, we have achieved a classification accuracy of $90.4\%\;and\;94.9\%\;with\;1.8\%$ rejection, respectively.

• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.239-248
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• 2010

Leukocyte in blood smear image provides significant information to doctors for diagnosis of patient health status. Therefore, it is necessary step to separate leukocyte from blood smear image among various blood cells for early disease prediction. In this paper, we present a saliency map and stepwise region merging based leukocyte segmentation method. Since leukocyte region has salient color and texture, we create a saliency map using these feature map. Saliency map is used for sub-image separation. Then, clustering is performed on each sub-image using mean-shift. After mean-shift is applied, stepwise region-merging is applied to particle clusters to obtain final leukocyte nucleus. The experimental results show that our system can indeed improve segmentation performance compared to previous researches with average accuracy rate of 71%.