• Journal of KIISE:Information Networking
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• v.33 no.3
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• pp.201-217
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• 2006

In this paper, we collected the physical traces from high speed Internet backbone traffic and analyze the various characteristics of the underlying packet traces. Particularly, our work is focused on analyzing the characteristics of an anomalous traffic. It is found that in our data, the anomalous traffic is caused by UDP session traffic and we determined that it was one of the Denial of Service attacks. In this work, we adopted the unsupervised machine learning algorithm to classify the network flows. We apply the k-means clustering algorithm to train the learner. Via the Cramer-Yon-Misses test, we confirmed that the proposed classification method which is able to detect anomalous traffic within 1 second can accurately predict the class of a flow and can be effectively used in determining the anomalous flows.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.23-34
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• 2008

The number of web service user is increasing steadily as web-based service is offered in various form. But, web service has a vulnerability such as SQL Injection, Parameter Injection and DoS attack. Therefore, it is required for us to develop Web IDS system and additionally to offer Rule-base intrusion detection/response mechanism against those attacks. However, existing Web IDS system didn't correspond properly on recent web attack mechanism because they didn't including suitable pre-processing procedure on huge web log data. Therfore, we propose an efficient web log pre-processing mechanism for enhancing rule based detection and improving the performance of web IDS base attack response system. Proposed algorithm provides both a field unit parsing and a duplicated string elimination procedure on web log data. And it is also possible for us to construct improved web IDS system.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.279-290
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• 2005

Recently, as the serious damage caused by DDoS attacks increases, the rapid detection and the proper response mechanisms are urgent. However, existing security mechanisms do not effectively defend against these attacks, or the defense capability of some mechanisms is only limited to specific DDoS attacks. In this paper, we propose a detection architecture against DDoS attack using data mining technology that can classify the latest types of DDoS attack, and can detect the modification of existing attacks as well as the novel attacks. This architecture consists of a Misuse Detection Module modeling to classify the existing attacks, and an Anomaly Detection Module modeling to detect the novel attacks. And it utilizes the off-line generated models in order to detect the DDoS attack using the real-time traffic. We gathered the NetFlow data generated at an access router of our network in order to model the real network traffic and test it. The NetFlow provides the useful flow-based statistical information without tremendous preprocessing. Also, we mounted the well-known DDoS attack tools to gather the attack traffic. And then, our experimental results show that our approach can provide the outstanding performance against existing attacks, and provide the possibility of detection against the novel attack.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.141-147
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• 2017

Recently there has been an increasing demand for developing a domestic software (S/W) for infrared signature generation to prevent technology leakage and match the domestic operating environment. In this study, we developed a S/W for infrared signature generation and presented its structures and functions for creating and analyzing the IR images of designated spectral bands. The proposed S/W generates IR images of an object through calculations of surface temperatures and IR signals including the self-emitted, surface reflected and path dependent radiances. Moreover, the proposed S/W includes the features of infrared threat analyses from the generated IR images including the infrared contrast radiant intensity (CRI), detection ranges or detection probability analyses, unlike the imported, commercial infrared signature generation S/W.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.15-22
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• 2020

In this study, we propose an improvement method that can create U-Net model which detect fine concrete cracks by applying a weighted loss function. Because cracks in concrete are a factor that threatens safety, it is important to periodically check the condition and take prompt initial measures. However, currently, the visual inspection is mainly used in which the inspector directly inspects and evaluates with naked eyes. This has limitations not only in terms of accuracy, but also in terms of cost, time and safety. Accordingly, technologies using deep learning is being researched so that minute cracks generated in concrete structures can be detected quickly and accurately. As a result of attempting crack detection using U-Net in this study, it was confirmed that it could not detect minute cracks. Accordingly, as a result of verifying the performance of the model trained by applying the suggested weighted loss function, a highly reliable value (Accuracy) of 99% or higher and a harmonic average (F1_Score) of 89% to 92% was derived. The performance of the learning improvement plan was verified through the results of accurately and clearly detecting cracks.

• Journal of Applied Biological Chemistry
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• v.57 no.4
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• pp.341-345
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• 2014

Fate of fenarimol, chlorothalinol, and ethoprophos sprayed to control disease and pest was studied in a agroforest culture field of Jangsu-gun, Jeollabuk-do, Korea. Concentrations of fenarimol, chlorothalinol, and ethoprophos in runoff water ranged mostly to 0.2 mg/L at the first rainfall-runoff event. And then was rapidly decreased than detection limit at 60 days after the application. The fenarimol and chlorothalonil residue in soil was dissipated to below detection limit at 30 days after the application. But ethoprophos was decreased to below detection limit at 135 days after the application. The concentrations of experimental pesticides were highly detected in agroforest culture field than in open culture field. It is assumed that experimental pesticides were strongly adsorbed by organic matter such as fulvic acid and humic acid.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.2
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• pp.137-145
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• 1998

In this paper, we propose a tube-hole center detection vision algorithm verifying the position of a tele-controlled robot and providing visual information for increasing reliability and efficiency in the diagnosis of steam generator (SG) tubes in nuclear power plant. A tele-controlled robot plays a role in carrying the probe used in inspecting the integrity of SG tubes. Thus accurately locating a tele-controlled robot on the desired tube-hole center is important issue for reliability of inspection. To do this work, we have to find the tube-hole center locations from the input image. At first, we apply the three-class segmentation method modified for this application. WE extract minimum bounding rectangles (MBRs) in the theresholded binary image. Second, for discriminating between MBR by tube and MBR by noise, we introduce the MBR rejection rules as knowledge-based rule set. MBRs are divided into the very dark region MBRs and the very bright region MBRs. In order to describe the region of complete tube-hole, the MBRs need a process of pairing each other. We then can find the tube-hole center from the paired MBR. For more accurately finding the tube-hole center in several sequential images, the centers of some frames need to be averaged. We tested the performance of our method using hundreds of real images.

• Journal of Biomedical Engineering Research
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• v.35 no.6
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• pp.211-218
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• 2014

The objective of this research is to develop an automatic algorithm based on electrocardiogram (ECG) to estimate slow-wave sleep (SWS). An algorithm is based on 7 indices extracted from heart rate on ECG which simultaneously recorded with standard full night polysomnography from 31 subjects. Those 7 indices were then applied to independent component analysis to extract a feature that discriminates SWS and other sleep stages. Overall Cohen's kappa, accuracy, sensitivity and specificity of the algorithm to detect 30s epochs of SWS were 0.52, 0.87, 0.70 and 0.90, respectively. The automatic SWS detection algorithm could be useful combining with existing REM and wake estimation technique on unattended home-based sleep monitoring.

• Journal of Microbiology and Biotechnology
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• v.17 no.4
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• pp.673-676
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• 2007

A two-step triplex PCR assay targeting the mecA, femA, and nuc genes was developed for the detection of methicillin resistance genes harbored by some Staphylococcus aureus isolates and for the simultaneous identification of such isolates at the species level. The triplex PCR revealed the presence of the femA and nuc genes in all the S. aureus isolates examined (n=105). Forty-four clinical isolates were mecA positive and no foodborne isolates were mecA positive. The PCR results had a 98 or 99% correlation with the results of PBP2a latex agglutination tests or oxacillin susceptibility tests, respectively.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.18
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• pp.7981-7984
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• 2014

Purpose: Colorectal cancer (CRC) screening with fecal occult blood testing (FOBT) has been associated with a reduction in CRC incidence and CRC-related mortality. However, a conventional FOBT requires stool collection and handling, which may be inconvenient for participants. The EZ-Detect$^{TM}$ (Siam Pharmaceutical Thailand) is a FDA-approved chromogen-substrate based FOBT which is basically a self-checked FOBT (no stool handling required). This study aimed to evaluate the accuracy of EZ-Detect for CRC detection. Methods: This prospective study was conducted in the Faculty of Medicine, Siriraj Hospital, Bangkok, Thailand between November 2013 and May 2014. Some 96 patients with histologically-proven CRC and 101 patients with normal colonoscopic findings were invited to perform self-checked FOBT according to the manufacturer's instructions. Results were compared with endoscopic and pathologic findings. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for CRC detection were calculated. Results: The present study revealed the sensitivity, specificity, PPV and NPV of this self-checked FOBT for CRC detection to be 41% (95% CI: 31-51), 97% (95% CI: 92-99), 93% (95% CI: 81-98) and 63% (95% CI: 55-70), respectively. The overall accuracy of the self-checked FOBT for identifying CRC was 70%. The sensitivity for CRC detection based on 7th AJCC staging was 29% for stage I, 32% for stage II and 50% for stage III/IV (P=0.19). The sensitivity was 33% for proximal colon and 42% for distal colon and rectal cancer (P=0.76). Notably, none of nine infiltrative lesions gave a positive FOBT. Conclusions: The self-checked FOBT had an acceptable accuracy of CRC detection except for infiltrative tumors. This home-administrated or 'DIY' do-it-yourself FOBT could be considered as one non-invasive and convenient tool for CRC screening.