• Journal of Korea Society of Industrial Information Systems
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• v.15 no.4
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• pp.17-27
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• 2010

Major IT vendors expect that cloud computing technology makes it possible to reduce the contents service cycle, speed up application deployment and skip the installation process, reducing operational costs, proactive management etc. However, cloud computing environment for massive content service solutions requires high-performance data processing to reduce the time of data processing and analysis. In this study, Efficient_Cloud_Processing_Scheme(ECPS) is proposed for allocation of resources for massive content services. For high-performance services, optimized resource allocation plan is presented using MapReduce programming techniques and association rules that is used to detect hidden patterns in data mining, based on levels of Hadoop platform(Infrastructure as a service). The proposed ECPS has brought more than 20% improvement in performance and speed compared to the traditional methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.312-320
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• 2023

Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.1-9
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• 2023

Malware files containing concealed malicious scripts have recently been identified within MS Office documents frequently. In response, this paper describes the design and implementation of a system that automatically detects malicious digital files using machine learning techniques. The system is proficient in identifying malicious scripts within MS Office files that exploit the OLE VBA macro functionality, detecting malicious scripts embedded within the CDH/LFH/ECDR internal field values through OOXML structure analysis, and recognizing abnormal CDH/LFH information introduced within the OOXML structure, which is not conventionally referenced. Furthermore, this paper presents a mechanism for utilizing the VirusTotal malicious script detection feature to autonomously determine instances of malicious tampering within MS Office files. This leads to the design and implementation of a machine learning-based integrated software. Experimental results confirm the software's capacity to autonomously assess MS Office file's integrity and provide enhanced detection performance for arbitrary MS Office files when employing the optimal machine learning model.

• Advanced Industrial SCIence
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• v.2 no.4
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• pp.11-16
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• 2023

The subway is a means of public transportation that plays an important role in the transportation system of modern cities. However, congestion often occurs due to sudden breakdowns and system outages, causing inconvenience. Therefore, in this paper, we conducted a study on failure prediction and prevention using machine learning to efficiently operate the subway system. Using UC Irvine's MetroPT-3 dataset, we built a subway breakdown prediction model using logistic regression. The model predicted the non-failure state with a high accuracy of 0.991. However, precision and recall are relatively low, suggesting the possibility of error in failure prediction. The ROC_AUC value is 0.901, indicating that the model can classify better than random guessing. The constructed model is useful for stable operation of the subway system, but additional research is needed to improve performance. Therefore, in the future, if there is a lot of learning data and the data is well purified, failure can be prevented by pre-inspection through prediction.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.651-666
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• 2024

Android devices employ lock screens with various authentication methods to protect user data. However, even with the lock screen active, the device can be accessed via the Android Debug Bridge(ADB), a powerful development tool that controls devices connected through USB. In this paper, we explore methods to bypass the lock screen security mechanism by leveraging the characteristics of ADB. To achieve this, we analyze ADB commands to categorize those that can severely impact the Android system and propose LockPickFuzzer, a fuzzing test tool that automatically explores ways to combine these commands to disable lock screen security. To demonstrate LockPickFuzzer's ability to detect security vulnerabilities using ADB, we conducted experiments on the Galaxy S23 and Pixel 8, both running Android 14. The results revealed two ADB command combinations that could either steal authentication information or bypass the lock screen. We submitted a report on these discovered vulnerabilities to the Samsung security team and received official acknowledgment (SVE-2023-1344) from Samsung Electronics for one ADB command combination that can be reproduced on user devices. LockPickFuzzer is a practical tool that operates automatically without user intervention and is expected to contribute to the effective detection of security vulnerabilities caused by ADB command combinations on Android devices.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.173-180
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• 2015

A passive type of fence security system was developed, which was based on electric charge detection technique. The implemented fence security system was installed at outskirts of greenhouse laboratory in the University of Seoul. The purpose of this research is to minimize false alarms by analyzing environmental noise. The existing system determines the intrusion alarm by analyzing the power of amplified signal, but the alarm was seriously affected by natural strong wind and heavy rainfall. The SAU(Signal Analysis Unit) sends input signals to remote server which displays intrusion alarm and stores all the information in database. The environmental noise such as temperature, humidity and wind speed was separately gathered to analyze a correlation with input signal. The input signal was analyzed for frequency characteristic using FFT(Fast Fourier Transform) and the algorithm that differentiate between intrusion alarm and environmental noise signal is improved. The proposed algorithm is applied for the site for one month as the same as the existing algorithm and the false alarm data was gathered and analyzed. The false alarm number was decreased by 98% after new algorithm was applied to the fence. The proposed algorithm improved the reliability at the field regarding environmental noise signal.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.08a
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• pp.163-163
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• 2010

중적외선 영역 ($3{\sim}5\;{\mu}m$)은 공기 중에 존재하는 이산화탄소나 수증기에 의해 흡수가 일어나지 않기 때문에 군사적으로 중요한 파장 영역이며, 야간에 적을 탐지하는데 응용되고 있다. InSb는 77 K에서 중적외선 파장 흡수에 적합한 밴드갭 에너지 (0.228 eV)를 갖고 있으며, 다른 화합물 반도체와 달리 전하 수송자 이동도 (전자: $10^6\;cm^2/Vs$, 정공: $10^4\;cm^2/Vs$)가 매우 빠르기 때문에 적외선 화상 감지기 재료로 매우 적합하다. 또한 현재 중적외선 영역대에서 널리 사용되는 HgCdTe (MCT)와 대등한 소자 성능을 나타냄과 동시에 낮은 기판 가격, 소자의 제작 용이성 때문에 MCT를 대체할 물질로 주목 받고 있다. 하지만, 기판과 절연막의 계면에 존재하는 결함 때문에 에너지 밴드갭 내에 에너지 준위를 형성하여 높은 누설 전류 특성을 보인다. 따라서 InSb 적외선 소자의 구현을 위하여 고품질의 절연막의 연구가 필수적이라고 할 수 있겠다. 절연막의 특성을 알아보기 위해, n형 InSb 기판에 플라즈마 화학 기상 증착법 (PECVD)을 이용하여 $SiO_2$, $Si_3N_4$를 증착하였으며, 증착 온도를 $120^{\circ}C$에서 $240^{\circ}C$까지 $40^{\circ}C$ 간격으로 변화하여 증착온도가 미치는 영향에 대하여 알아보았다. 절연막과 기판의 계면 특성을 분석하기 위하여 77 K에서 커패시턴스-전압 (C-V) 분석을 하였으며, 계면 트랩 밀도는 Terman method를 이용하여 계산하였다 [1]. $Si_3N_4$를 증착하였을 경우, $120{\sim}240^{\circ}C$의 증착 온도에서 $2.4{\sim}4.9{\times}10^{12}\;cm^{-2}eV^{-1}$의 계면 트랩 밀도를 가졌으며, 증착 온도가 증가할수록 계면 트랩 밀도가 증가하는 경향을 보였다. 또한 모든 증착 온도에서 flat band voltage가 음의 전압으로 이동하였다. $SiO_2$의 경우 $120{\sim}200^{\circ}C$의 증착온도에서 $7.1{\sim}7.3{\times}10^{11}\;cm^{-2}eV^{-1}$의 계면 트랩 밀도 값을 보였으나, $240^{\circ}C$ 이상에서 계면 트랩밀도가 $12{\times}10^{11}\;cm^{-2}eV^{-1}$로 크게 증가하였다. $SiO_2$ 절연막을 사용함으로써, $Si_3N_4$ 대비 약 25% 정도 낮은 계면 트랩 밀도를 얻을 수 있었으며, 모든 증착 온도에서 양의 전압으로 flat band voltage가 이동하였다. 두 절연막에 대한 계면 트랩의 원인을 분석하기 위하여 XPS 측정을 진행하였으며, 깊이에 따른 조성 분석을 하였다. 본 실험에서 최적화된 $SiO_2$ 절연막을 이용하여 InSb 소자의 pn 접합 연구를 진행하였다. Be+ 이온 주입을 진행하고, 급속열처리(RTA) 공정을 통하여 p층을 형성하였다. -0.1 V에서 16 nA의 누설 전류 값을 보였으며, $2.6{\times}10^3\;{\Omega}\;cm^2$의 RoA (zero bias resistance area)를 얻을 수 있었다.

• The Journal of the Acoustical Society of Korea
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• v.41 no.4
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• pp.435-445
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• 2022

In this study, the reverberation of a bistatic sonar operated in southeastern coast in the East Sea in July 2020 was analyzed. The reverberation sensor data were collected through an LFM sound source towed by a research vessel and a horizontal line array receiver 1 km to 5 km away from it. The reverberation sensor data was analyzed by various methods including geo-plot after signal processing. Through this, it was confirmed that the angle reflected from the sound source through the scatterer to the receiver has a dominant influence on the distribution of the reverberation sound, and the probability distribution characteristics of bistatic sonar reverberation varies for each beam. In addition, parametric factors of K distribution and Rayleigh distribution were estimated from the sample through moment method estimation. Using the Kolmogorov-Smirnov test at the confidence level of 0.05, the distribution probability of the data was analyzed. As a result, it could be observed that the reverberation follows a Rayleigh probability distribution, and it could be estimated that this was the effect of a low reverberation to noise ratio.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.617-629
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• 2020

In order to overcome the limitations of the rule-based intrusion detection system due to changes in Internet computing environments, the emergence of new services, and creativity of attackers, network anomaly detection (NAD) using machine learning and deep learning technologies has received much attention. Most of these existing machine learning and deep learning technologies for NAD use supervised learning methods to learn a set of training data set labeled 'normal' and 'attack'. This paper presents the feasibility of the unsupervised learning AutoEncoder(AE) to NAD from data sets collecting of secured network traffic without labeled responses. To verify the performance of the proposed AE mode, we present the experimental results in terms of accuracy, precision, recall, f1-score, and ROC AUC value on the NSL-KDD training and test data sets. In particular, we model a reference AE through the deep analysis of diverse AEs varying hyper-parameters such as the number of layers as well as considering the regularization and denoising effects. The reference model shows the f1-scores 90.4% and 89% of binary classification on the KDDTest+ and KDDTest-21 test data sets based on the threshold of the 82-th percentile of the AE reconstruction error of the training data set.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.117-124
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• 2018

This paper describes the localization development of an axial fan for KM-SAM multi-function radar. The multi-function radar, which is constantly affected by the external environment, is a key instrument for detecting and tracking low and medium altitude threat targets. Operating this equipment smoothly requires a fan for controlling the internal temperature and humidity. Presently, all such fans are imported. To solve these problems, localization development research was proposed. The development of localization includes analysis of requirements through review of related technical reports such as original equipment and system equipment specification, prototype design, and verification of design requirement through performance test and environmental test. The study results are described. The blower consisted of an axial fan with guide vanes and the motor was designed to generate a maximum airflow of 970 CFM and a wind pressure of 4.8 IWG. Six prototypes were manufactured for performance evaluation. In addition, for reliable data acquisition, AC power supply, fan performance tester and data acquisition equipment were designed and tested. All prototypes were verified as having design requirements equal to or better than those of imports.