• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.305-313
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• 2018

With the explosive growth of smart phones and efficiency, the Android of an open mobile operating system is gradually increasing in the use and the availability. Android systems has proven its availability and stability in the mobile devices, the home appliances's operating systems, the IoT products, and the mechatronics. However, as the usability increases, the malicious code based on Android also increases exponentially. Unlike ordinary PCs, if malicious codes are infiltrated into mobile products, mobile devices can not be used as a lock and can be leaked a large number of personal contacts, and can be lead to unnecessary billing, and can be cause a huge loss of financial services. Therefore, we proposed a method to detect and delete malicious files in real time in order to solve this problem. In this paper, we also designed a method to detect and delete malicious codes in a more effective manner through the process of installing Android-based applications and signature-based malicious code detection method. The method we proposed and designed can effectively detect malicious code in a limited resource environment, such as mobile environments.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.6
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• pp.339-348
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• 2006

Recently, many researches on detecting and responding worms due to the fatal infrastructural damages explosively damaged by automated attack tools, particularly worms. Network service vulnerability exploiting worms have high propagation velocity, exhaust network bandwidth and even disrupt the Internet. Previous worm researches focused on signature-based approaches however these days, approaches based on behavioral features of worms are more highlighted because of their low false positive rate and the attainability of early detection. In this paper, we propose a Distributed Worm Detection Model based on packet marking. The proposed model detects Worm Cycle and Infection Chain among which the behavior features of worms. Moreover, it supports high scalability and feasibility because of its distributed reacting mechanism and low processing overhead. We virtually implement worm propagation environment and evaluate the effectiveness of detecting and responding worm propagation.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.7
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• pp.275-288
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• 2019

In this paper, we consider a rumor source inference problem when sufficiently many nodes heard the rumor in the network. This is an important problem because information spread in networks is fast in many real-world phenomena such as diffusion of a new technology, computer virus/spam infection in the internet, and tweeting and retweeting of popular topics and some of this information is harmful to other nodes. This problem has been much studied, where it has been shown that the detection probability cannot be beyond 31% even for regular trees if the number of infected nodes is sufficiently large. Motivated by this, we study the impact of query that is asking some additional question to the candidate nodes of the source and propose budget assignment algorithms of a query when the network administrator has a finite budget. We perform various simulations for the proposed method and obtain the detection probability that outperforms to the existing prior works.

• Journal of Veterinary Science
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• v.21 no.2
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• pp.14.1-14.10
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• 2020

African swine fever (ASF) is a highly contagious disease of domestic pigs and wild boars (WBs). Without a vaccine, early antibody and antigen detection and rapid diagnosis are crucial for the effective prevention of the disease and the employment of control measures. In Sardinia, where 3 different suid populations coexisted closely for a long time, the disease persists since 1978. The recent ASF eradication plan involves more stringent measures to combat free-ranging pigs and any kind of illegality in the pig industry. However, critical issues such as the low level of hunter cooperation with veterinary services and the time required for ASF detection in the WBs killed during the hunting season still remain. Considering the need to deliver true ASF negative carcasses as early as possible, this study focuses on the evaluation and validation of a duplex pen-side test that simultaneously detects antibodies and antigens specific to ASF virus, to improve molecular diagnosis under field conditions. The main goal was to establish the specificity of the two pen-side tests performed simultaneously and to determine their ability to detect the true ASF negative carcasses among the hunted WBs. Blood and organ samples of the WBs hunted during the 2018/2019 hunting seasons were obtained. A total of 160 animals were tested using the pen-side kit test; samples were collected for virological and serological analyses. A specificity of 98% was observed considering the official laboratory tests as gold standards. The new diagnostic techniques could facilitate faster and cost-effective control of the disease.

• Korean Journal of Veterinary Research
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• v.63 no.2
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• pp.19.1-19.6
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• 2023

Rapid immunochromatography test (RICT) kits are commonly used for the diagnosis of canine parvovirus (CPV) because of their rapid turnaround time, simplicity, and ease of use. However, the potential for cross-reactivity and low sensitivity can yield false-positive or false-negative results. There are 4 genotypes of CPV. Therefore, evaluating the performance and reliability of RICT kits for CPV detection is essential to ensure accurate diagnosis for appropriate treatment. In this study, we evaluated the performance of commercial RICT kits in the diagnosis of all CPV genotypes. The cross-reactivity of 6 commercial RICT kits was evaluated using 8 dog-related viruses and 4 bacterial strains. The limit of detection (LOD) was measured for the 4 genotypes of CPV and feline panleukopenia virus. The tested kits showed no cross-reactivity with the 8 dog-related viruses or 4 bacteria. Most RICT kits showed strong positive results for CPV-2 variants (CPV-2a, CPV-2b, and CPV-2c). However, the 2 kits produced negative results for CPV-2 or CPV-2b at a titer of 10⁵ FAID₅₀/mL, which may result in inaccurate diagnoses. Therefore, some kits need to improve their LOD by increasing their binding efficiency to detect all CPV genotypes.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.59-70
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• 2024

Background: The COVID-19 pandemic (the form of coronaviruses) developed at the end of 2019 and spread rapidly to almost every corner of the world. It has infected around 25,334,339 of the world population by the end of September 1, 2020 [1] . It has been spreading ever since, and the peak specific to every country has been rising and falling and does not seem to be over yet. Currently, the conventional RT-PCR testing is required to detect COVID-19, but the alternative method for data archiving purposes is certainly another choice for public departments to make. Researchers are trying to use medical images such as X-ray and Computed Tomography (CT) to easily diagnose the virus with the aid of Artificial Intelligence (AI)-based software. Method: This review paper provides an investigation of a newly emerging machine-learning method used to detect COVID-19 from X-ray images instead of using other methods of tests performed by medical experts. The facilities of computer vision enable us to develop an automated model that has clinical abilities of early detection of the disease. We have explored the researchers' focus on the modalities, images of datasets for use by the machine learning methods, and output metrics used to test the research in this field. Finally, the paper concludes by referring to the key problems posed by identifying COVID-19 using machine learning and future work studies. Result: This review's findings can be useful for public and private sectors to utilize the X-ray images and deployment of resources before the pandemic can reach its peaks, enabling the healthcare system with cushion time to bear the impact of the unfavorable circumstances of the pandemic is sure to cause

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1478-1499
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• 2024

Coronavirus disease (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. COVID-19 become an active epidemic disease due to its spread around the globe. The main causes of the spread are through interaction and transmission of the droplets through coughing and sneezing. The spread can be minimized by isolating the susceptible patients. However, it necessitates remote monitoring to check the breathing issues of the patient remotely to minimize the interactions for spread minimization. Thus, in this article, we offer a wearable-IoTs-centered framework for remote monitoring and recognition of the breathing pattern and abnormal breath detection for timely providing the proper oxygen level required. We propose wearable sensors accelerometer and gyroscope-based breathing time-series data acquisition, temporal features extraction, and machine learning algorithms for pattern detection and abnormality identification. The sensors provide the data through Bluetooth and receive it at the server for further processing and recognition. We collect the six breathing patterns from the twenty subjects and each pattern is recorded for about five minutes. We match prediction accuracies of all machine learning models under study (i.e. Random forest, Gradient boosting tree, Decision tree, and K-nearest neighbor. Our results show that normal breathing and Bradypnea are the most correctly recognized breathing patterns. However, in some cases, algorithm recognizes kussmaul well also. Collectively, the classification outcomes of Random Forest and Gradient Boost Trees are better than the other two algorithms.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.143-145
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• 1999

최근 네트워크 기술의 발달로 컴퓨터 바이러스의 종류가 급속도로 증가하고 있음은 물론, 그 확산이 빨라지고 있으며 감염 경로 또한 매우 다양해졌다. 따라서 그만큼 자주 갱신되는 백신을 사용자들이 매번 재설치 해야하는 번거로움이 있다. 또한 현재의 치료 방법은 하나의 바이러스를 치료하기 위해 모든 바이러스를 치료할 수 있는 백신을 실행시켜야 한다. 이동에이전트는 한 기계에서 다른 기계로 이동할 수 있는 프로그램으로 때와 장소를 선택하여 이동할 수 있고 원하는 시간에 실행하거나 프로그램 자체를 또 다른 기계로 옮겨서 실행토록 할 수 도 잇다. 이러한 이동 에이전트 패러다임과 지능형 에이전트를 이용하여 위의 문제점을 해결할 수 있으리라고 보여진다.

• 한국생물공학회:학술대회논문집
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• 2003.10a
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• pp.220-222
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• 2003

Legislation enacted worldwide to regulate the content of genetically modified organisms (GMOs) in crops, foods, and ingredients, reliable and sensitive methods for GMO detection have been developed. Proteins produced in GMO plants can be determined by qualitative and quantitative analyses and thus GMO designation has performed exactly. Target proteins selected in this study were neomycin phosphotransferase II (NPTII), 5-enolpyruvylshikimate-3-phosphate synthase (CP4 EPSPS), cucumber mosaic virus(CMV), and phosphinothricin acetyltransferase (PAT). Analytical method employing western blotting was used for final characterization.

• 대한예방의학회:학술대회논문집
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• 1995.10a
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• pp.123-124
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• 1995