• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1365-1373
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• 2019

Meltdown and Spectre are vulnerabilities that exploit out-of-order execution and speculative execution techniques to read memory regions that are not accessible with user privileges. OS patches were released to prevent this attack, but older systems without appropriate patches are still vulnerable. Currently, there are some research to detect Meltdown and Spectre attacks, but most of them proposed dynamic analysis methods. Therefore, this paper proposes a binary signature that can be used to detect Meltdown and Spectre malware without executing them. For this, we collected 13 malicious codes from GitHub and performed binary pattern analysis. Based on this, we proposed a static detection method for Meltdown and Spectre malware. Our results showed that the method identified all the 19 attack files with 0.94% false positive rate when applied to 2,317 normal files.

• Journal of the Korean Society for Nondestructive Testing
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• v.16 no.3
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• pp.155-161
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• 1996

For the condition monitoring of the journal bearing in rotating machinery, a system for their diagnosis by acoustic emission(AE) was developed. AE has been used to detect abnormal conditions in the bearing system. It was found from the field application study as well as the laboratory experiment using a simulated journal bearing system that AE RMS voltage was the most efficient parameter for the purpose of current study. Based on the above results, algorithms and judgement criteria for the diagnosis system was established. The system is composed of four parts as follows: the sensing part including AE sensor and preamplifier, the signal processing part for RMS-to-DC conversion to measure AE ms voltage, the interface part for transferring RMS voltage data into PC using A/D converter, and the software part including the graphic display of bearing conditions and the diagnosis program.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.312-318
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• 2011

USN technology will be applied to various fields such as logistics, transportation, government, health, welfare and environment and will be settled down by basic infrastructure of a future society. In this study, we analyzed sensor networks structure based on IEEE 802.15.4 and implemented the sensor monitoring system using Zigbee modules. For implementation of real-time sensor monitoring system, we designed Linux-based development environment and the sensor-specific component. The result of this paper may be utilized in such areas lighting system, intrusion detection, fire detection, detection and notification of abnormal conditions.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.316-321
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• 2003

Recently, support vector learning attracts great interests in the areas of pattern classification, function approximation, and abnormality detection. It is well-known that among the various support vector learning methods, the so-called no-versions are particularly useful in cases that we need to control the total number of support vectors. In this paper, we consider the problem of function approximation utilizing both predetermined basis functions and a no-version support vector learning called $\nu-SVR$. After reviewing $\varepsilon-SVR$, $\nu-SVR$, and a semi-parametric approach, this paper presents an extension of the conventional $\nu-SVR$ method toward the direction that can utilize Predetermined basis functions. Moreover, the applicability of the presented method is illustrated via an example.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.19-27
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• 2020

In this paper, we propose a machine learning method for diagnosing the failure of a gas pressure regulator. Originally, when implementing a machine learning model for detecting abnormal operation of a facility, it is common to install sensors to collect data. However, failure of a gas pressure regulator can lead to fatal safety problems, so that installing an additional sensor on a gas pressure regulator is not simple. In this paper, we propose various machine learning approach for diagnosing the abnormal operation of a gas pressure regulator with only the flow rate and gas pressure data collected from a gas pressure regulator itself. Since the fault data of a gas pressure regulator is not enough, the model is trained in all classes by applying the over-sampling method. The classification model was implemented using Gradient boosting, 1D Convolutional Neural Networks, and LSTM algorithm, and gradient boosting model showed the best performance among classification models with 99.975% accuracy.

• Smart Media Journal
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• v.12 no.7
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• pp.9-17
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• 2023

Plant diseases and pests affect the growth of various plants, so it is very important to identify pests at an early stage. Although many machine learning (ML) models have already been used for the inspection and classification of plant pests, advances in deep learning (DL), a subset of machine learning, have led to many advances in this field of research. In this study, disease and pest inspection of abnormal crops and maturity classification were performed for normal crops using YOLOX detector and MobileNet classifier. Through this method, various plant pest features can be effectively extracted. For the experiment, image datasets of various resolutions related to strawberries, peppers, and tomatoes were prepared and used for plant pest classification. According to the experimental results, it was confirmed that the average test accuracy was 84% and the maturity classification accuracy was 83.91% in images with complex background conditions. This model was able to effectively detect 6 diseases of 3 plants and classify the maturity of each plant in natural conditions.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.4
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• pp.60-68
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• 2003

In this study, a steady state performance analysis program was developed for a turbofan engine, and its performance was analyzed at installed conditions. For the purpose of evaluation, the developed program was compared with the performance data provided by the engine manufacturer. It was confirmed that the developed program was reliable because the results by the developed program were well agreed with those by the engine manufacturer within 3.5%. The non-linear GPA(Gas Path Analysis) program for performance diagnostics were developed, and selection of optimal measurement variables was studied. Furthermore, in order to investigate effects of the number and the kind of measurement variables, the non-linear GPA was analyzed with various measurement sets. Finally, the measurement parameters selected in the previous step were applied to the fault detection analysis of the 2-spool separate flow type turbofan engine.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.584-588
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• 2019

Wireless body area network (WBAN) is a networking technology that enables early detection of abnormal health conditions, real-time medical monitoring, and telemedicine support systems. The internet of things (IoT) for healthcare, which has become an issue recently, is one of the most promising areas for improving the quality of human life. It must meet the high QoS requirements of the medical communication system like any other communication system. Therefore, the bit error rate (BER) threshold was chosen to accommodate the QoS requirements of the WBAN communication system. In this paper, we calculated BER performance of WBAN channel using IR-UWB PPM modulation and analyzed link budget and system margin of WBAN according to various system parameters.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.283-285
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• 2020

본 논문에서는 시스템이 비정상적인 상태에 진입하였는지를 판단하여 공격에 대한 탐지를 효율적으로 수행할 수 있는 하드웨어 기반 보안 모니터링 기술에 대해 소개한다. 먼저 이벤트 기반으로 커널을 보호하는 모니터링 기법들에 대해 알아볼 것이다. 최종적으로 다양한 이벤트를 유연하게 모니터링할 수 있는 기법을 살펴보고, 이를 바탕으로 보안 하드웨어 모니터링 기법의 향후 연구방향을 모색하고자 한다.