• Journal of the Korea Society of Computer and Information
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• v.17 no.5
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• pp.21-28
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• 2012

Recently, the necessity for good techniques of detecting network traffic attack has increased. In this paper, we suggest a new method of detecting abnormal patterns of network traffic data by visualizing their IP and port information into two dimensional images. The proposed approach first generates four 2D images from IP data of transmitters and receivers, and makes one 2D image from port data. Analyzing those images, it then extracts their major features such as linear patterns or high intensity values, and determines if traffic data contain DDoS or DoS Attacks. To comparatively evaluate the performance of the proposed algorithm, we show that our abnormal pattern detection method outperforms the existing algorithm in terms of accuracy and speed.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.463-468
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• 2021

Although the IoT is showing explosive growth due to the development of technology and the spread of IoT devices and activation of services, serious security risks and financial damage are occurring due to the activities of various botnets. Therefore, it is important to accurately and quickly detect the activities of these botnets. As security in the IoT environment has characteristics that require operation with minimum processing performance and memory, in this paper, the minimum characteristics for detection are selected, and KNN (K-Nearest Neighbor), Naïve Bayes, Decision Tree, Random A comparative study was conducted on the performance of machine learning algorithms such as Forest to detect botnet activity. Experimental results using the Bot-IoT dataset showed that KNN can detect DDoS, DoS, and Reconnaissance attacks most effectively and efficiently among the applied machine learning algorithms.

• Physical Therapy Korea
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• v.6 no.1
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• pp.47-61
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• 1999

The purpose of this study was to investigate factors influencing on the early treatment of children with developmental disability. Data was collected from 102 mothers of children with developmental disability who were treated at 4 rehabilitation facilities in Kyunggi-Do and Kangwon-Do. The results were as follows: 1) Of a total of 102, 63 children began to receive rehabilitation therapy during the period 0~12 months (early treatment group), 38 children after 1 year of age (delayed early treatment group). 2) There were statistically significant differences between the early treatment group and delayed early treatment group for prematurity, low birth weight, the time to discover developmental abnormalities, the time of first diagnosis, and first treatment (p<0.05). 3) There were no statistically significant differences in the two groups for level of education, economic status, risk factors (except prematurity and birth weight), home care, family's cooperation and commuting time (p>0.05). Based on this study, the important factors for early treatment were early detection, early diagnosis and constant follow-up for high-risk babies.

• Journal of Applied Reliability
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• v.15 no.2
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• pp.131-138
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• 2015

In modern warfare, the number of unmanned systems grow faster than any other weapon systems. Therefore, it is very important to predict and measure the combat effectiveness (CE) of unmanned weapon systems in battlefield for deciding defense budget to acquire those systems. In general, quantitative calculation of weapon effectiveness under complicated battlefield is difficult based on the future network centric warfare. Hence, many papers studied how to measure the combat effectiveness and tried to study a lot of related issues about it. However, there are few papers dealing with the relationship between the UGV (Unmanned Ground Vehicle)'s performance and CE in a ground battlefield. In this paper, we do the sensitivity analysis based on a given scenario in a small unit battle. In order to do that, we developed simulation model using AnyLogic and changed the input parameters such as detection and hitting probabilities. We also assess the simulation outputs according to the variation of input parameters. The MOE used in this simulation model output is survival ratio for Blue force. We hope that this paper will be useful to find which input variable is more effective to increase combat effectiveness in a small unit ground battlefield.

• Journal of Veterinary Clinics
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• v.30 no.4
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• pp.236-240
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• 2013

A total of 170 litters (575 samples) of aborted and stillbirth fetuses submitted to the Gyeongsangbuk-Do Veterinary Service Laboratory (GVSL) between January 2006 and December 2010 from pig farms in Gyeongbuk province were studied to identify porcine abortion- and stillbirth-associated viruses such as Porcine parvovirus (PPV), Encephalomyocarditis Virus (EMCV), Japanese Encephalitis Virus (JEV), Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), and Aujeszky's Disease Virus (ADV). Virus was not detected by PCR in 36 litters, but viral antibody was detected by HI and ELISA in 93 litters. The majority of etiological viruses were PPV (67 litters, 39.4%), EMCV (50 litters, 29.4%), PRRSV (15 litters, 8.8%), and JEV (11 litters, 6.5%); ADV was not detected by either PCR or ELISA. Single infection occurred in 52 litters (30.6%), co-infection occurred in 41 litters (24.1%), and unknown cases with no detection of any of the five viruses occurred in 77 litters (45.3%).

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.2
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• pp.53-58
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• 2005

Points of sharp variations in images are the most important components when we analyze singularities of images. And they include a variety of information about the image's location and shape etc. So a lot of researches for detecting those edges have been continuing even now and at the early stage of the research, edge detection operators used relation among neighborhood pixels. However, such methods do not have excellent performance in the image which exists noise and can not detect edge selectively. In the meantime, the wavelet transform which is presented as a new technique of signal processing field is able to detect multiscale edge and is being applied widely in many fields that analyze singularities such as edge. For this reason, in this paper we detected image's line-edge elements with 2-D wavelet function, which is independent of line's width, in mixed noise environment.

• Journal of Korea Multimedia Society
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• v.16 no.8
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• pp.954-961
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• 2013

To enhance the efficiency of network, content-centric networking (CCN), one of future Internet architectures, allows network nodes to temporally cache transmitted contents and then to directly respond to request messages which are relevant to previously cached contents. Also, since CCN uses a hierarchical content-name, not a host identity like source/destination IP address, for request/response packet routing and CCN request message does not include requester's information for privacy protection, contents-providers/ network nodes can not identify practical requesters sending request messages. So to send back relevant contents, network nodes in CCN records both a request message and its incoming interfaces on Pending Interest Table (PIT). Then the devices refer PIT to return back a response message. If PIT is exhausted, the device can not normally handle request/response messages anymore. Hence, it is needed to detect/react attack to exhaust PIT. Hence, in this paper, we propose improved detection/reaction schemes against attacks to exhaust PIT. In practice, for fine-grained control, this proposal is applied to each incoming interface. Also, we propose the message framework to control attack traffic and evaluate the performance of our proposal.

• Parasites, Hosts and Diseases
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• v.50 no.2
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• pp.103-111
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• 2012

Intestinal giant-cystic disease (IGCD) of the Israel carp (Cyprinus carpio nudus) has been recognized as one of the most serious diseases afflicting inland farmed fish in the Republic of Korea, and Thelohanellus kitauei has been identified as the causative agent of the disease. Until now, studies concerning IGCD caused by T. kitauei in the Israel carp have been limited to morphological and histopathological examinations. However, these types of diagnostic examinations are relatively time-consuming, and the infection frequently cannot be detected in its early stages. In this study, we cloned the full-length 18S rRNA gene of T. kitauei isolated from diseased Israel carps, and carried out molecular identification by comparing the sequence with those of other myxosporeans. Moreover, conventional PCR and real-time quantitative PCR (qPCR) using oligonucleotide primers for the amplification of 18S rRNA gene fragment were established for further use as methods for rapid diagnosis of IGCD. Our results demonstrated that both the conventional PCR and real-time quantitative PCR systems applied herein are effective for rapid detection of T. kitauei spores in fish tissues and environmental water.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.11
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• pp.1205-1216
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• 2015

Since concentration of greenhouse gas increases continuously from human's fossil fuel use, urbanization, and cultivation, it is trend that climate change is appearing. In Addition, in 20th century, occurrence of disaster is accidental and huge, and damage level also increases gradually. Therefore, in order to preserve the territory and to protect people's life and property against new type disasters, disaster detection satellite (payloads) development is required urgently. In this paper, we conduct a research and development for the prompt preemptive action when occurred a disaster, in particularly, about the disaster observation optimized at Korea's geographical features for the irregular future disasters. For the payload design which is specialized detect disasters, we create a tech tree of satellite imagery applications based 10 disaster types, and analyze the satellite sensor technologies referred to Landsat-8, Worldview-3 and ALOS-2.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.236-241
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• 1998

The NOAA AVHRR remote sense SST data, collected by the National Fisheries Research and Development Institute (NFRDI), are analyzed in order to understand the spatial and temporal distributions of SST in the seas adjacent to Korea. Our study is based on 10-day SST images during last 7 years (1991-1997). For a time series analysis of multiple 557 images, all of images must be aligned exactly at the same position by adjusting the scales and positions of each SST image. We devised an algorithm which yields automatic detections of cloud pixels from multiple SST images. The cloud detection algorithm is based on a physical constraint that SST anomalies in the ocean do not exceed certain limits (we used $\pm$ 3$^{\circ}C$ as a criterion of SST anomalies). The remote sense SST data are tuned by comparing remote sense data with observed SST at coastal stations. Seasonal variations of SST are studied by harmonic fit of SST normals at each pixel. The SST anomalies are studied by statistical method. We found that the SST anomalies are rather persistent with time scales between 1 and 2 months. Utilizing the persistency of SST anomalies, we devised an algorithm for a prediction of future SST Model fit of SST anomalies to the Markov process model yields that autoregression coefficients of SST anomalies during a time elapse of 10 days are between 0.5 and 0.7. We plan to improve our algorithms of automatic cloud pixel detection and prediction of future SST. Our algorithm is expected to be incorporated to the operational real time service of SST around Korea.