• ETRI Journal
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• v.44 no.4
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• pp.654-671
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• 2022

Accidental falls frequently occur during activities of daily living. Although many studies have proposed various accident detection methods, no high-performance accident detection system is available. In this study, we propose a method for integrating data and accident detection algorithms presented in existing studies, collect new data (from two stunt performers and 15 people over age 60) using a developed wearable device, demonstrate new features and related accident detection algorithms, and analyze the performance of the proposed method against existing methods. Comparative analysis results show that the newly defined features extracted reflect more important risk factors than those used in existing studies. Further, although the traditional algorithms applied to integrated data achieved an accuracy (AC) of 79.5% and a false positive rate (FPR) of 19.4%, the proposed accident detection algorithms achieved 97.8% AC and 2.9% FPR. The high AC and low FPR for accidental falls indicate that the proposed method exhibits a considerable advancement toward developing a commercial accident detection system.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.82-91
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• 2023

In 2020 when the COVID-19 pandemic began in full-scale, the WHO Director-General warned of the dangers of an infodemic. The infodemic is a phenomenon in which false information spreads rapidly like an epidemic and causes chaos, and it was noted that the COVID-19 pandemic is not just limited to health problems, but also linked to a variety of issues such as human rights, economic inequality, various discrimination, hate speech, fake news, global governance etc. In the field of education, it is necessary to think about how to connect this global situation with school classes. Accordingly, this study suggested the direction for global citizenship education by analyzing how the infodemic spreads on Korean social media with the case of the recent global COVID-19 pandemic. According to the research results, the rate of negative emotions was higher than positive ones in the emotions that generate infodemic, while anxiety and anger were focused among negative emotions. In addition, the infodemic tended to spread widely with the feelings of anger rather than anxiety, and the feelings of anger led to advocating aggressive policies against certain country and regions. Therefore, global citizenship education is required to focus on a sense of duty and responsibility as a citizen, not on the level of national identity based on an exclusive sense of belonging. Furthermore, global citizenship education needs to lead to presenting a blueprint for education in a way that can enhance the awareness of the global community for joint response to global challenges and realize common prosperity based on sustainability and justice.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.47.4-48
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• 2021

Gravitationally lensed Type Ia supernovae may be the next frontier in cosmic probes, able to deliver independent constraints on dark energy, spatial curvature, and the Hubble constant. Measurements of time delays between the multiple images become more incisive due to the standardized candle nature of the source, monitoring for months rather than years, and partial immunity to microlensing. While currently extremely rare, hundreds of such systems should be detected by upcoming time-domain surveys. Others will have the images spatially unresolved, with the observed lightcurve a superposition of time delayed image fluxes. We investigate whether unresolved images can be recognized as lensed sources given only lightcurve information and whether time delays can be extracted robustly. We develop a method that we show can identify these systems for the case of lensed Type Ia supernovae with two images and time delays exceeding ten days. When tested on such an ensemble the method achieves a false positive rate of ≲5%, and measures the time delays with the completeness of ≳93% and with a bias of ≲0.5% for time delay ≳10 days. Since the method does not assume a template of any particular type of SN, the method has the potential to work on other types of lensed SNe systems and possibly on other transients.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.54-57
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• 2022

Korea has a vulnerable environment from the risk of wildfires, which causes great damage every year. To prevent this, a lot of manpower is being used, but the effect is insufficient. If wildfires are detected and extinguished early through artificial intelligence technology, damage to property and people can be prevented. In this paper, we studied the object double detection method with the goal of minimizing the data collection and processing process that occurs in the process of creating an object detection model to minimize the damage of wildfires. In YOLOv5, the original image is primarily detected through a single model trained on a limited image, and the object detected in the original image is cropped through Crop. The possibility of improving the false positive object detection rate was confirmed through the object double detection method that re-detects the cropped image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.704-719
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• 2024

Botnet pandemics are becoming more prevalent with the growing use of mobile phone technologies. Mobile phone technologies provide a wide range of applications, including entertainment, commerce, education, and finance. In addition, botnet refers to the collection of compromised devices managed by a botmaster and engaging with each other via a command server to initiate an attack including phishing email, ad-click fraud, blockchain, and much more. As the number of botnet attacks rises, detecting harmful activities is becoming more challenging in handheld devices. Therefore, it is crucial to evaluate mobile botnet assaults to find the security vulnerabilities that occur through coordinated command servers causing major financial and ethical harm. For this purpose, we propose a hybrid analysis approach that integrates permissions and API and experiments on the machine-learning classifiers to detect mobile botnet applications. In this paper, the experiment employed benign, botnet, and malware applications for validation of the performance and accuracy of classifiers. The results conclude that a classifier model based on a simple decision tree obtained 99% accuracy with a low 0.003 false-positive rate than other machine learning classifiers for botnet applications detection. As an outcome of this paper, a hybrid approach enhances the accuracy of mobile botnet detection as compared to static and dynamic features when both are taken separately.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.59-66
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• 2018

The performance of a network intrusion detection system using the machine learning method depends heavily on the composition and the size of the feature set. The detection accuracy, such as the detection rate or the false positive rate, of the system relies on the feature composition. And the time it takes to train and detect depends on the size of the feature set. Therefore, in order to enable the system to detect intrusions in real-time, the feature set to beused should have a small size as well as an appropriate composition. In this paper, we show that the size of the feature set can be further reduced without decreasing the detection rate through using Pearson correlation coefficient between features along with the multi-objective genetic algorithm which was used to shorten the size of the feature set in previous work. For the evaluation of the proposed method, the experiments to classify 10 kinds of attacks and benign traffic are performed against NSL_KDD data set.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.501-511
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• 2022

In 2021, ransomware attacks became popular, and the number is rapidly increasing every year. Since PowerShell is used as the primary ransomware technique, the need for PowerShell-based malware detection is ever increasing. However, the existing detection techniques have limits in that they cannot detect obfuscated scripts or require a long processing time for deobfuscation. This paper proposes a simple and fast deobfuscation method and a deep learning-based classification model that can detect PowerShell-based malware. Our technique is composed of Word2Vec and a convolutional neural network to learn the meaning of a script extracting important features. We tested the proposed model using 1400 malicious codes and 8600 normal scripts provided by the AI-based PowerShell malicious script detection track of the 2021 Cybersecurity AI/Big Data Utilization Contest. Our method achieved 5.04 times faster deobfuscation than the existing methods with a perfect success rate and high detection performance with FPR of 0.01 and TPR of 0.965.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.6
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• pp.279-290
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• 2013

In digital forensics log files have been stored as a form of large data for the purpose of tracing users' past behaviors. It is difficult for investigators to manually analysis the large log data without clues. In this paper, we propose a text mining technique for extracting intrusion logs from a large log set to recommend reliable evidences to investigators. In the training stage, the proposed method extracts intrusion association words from a training log set by using Apriori algorithm after preprocessing and the probability of intrusion for association words are computed by combining support and confidence. Robinson's method of computing confidences for filtering spam mails is applied to extracting intrusion logs in the proposed method. As the results, the association word knowledge base is constructed by including the weights of the probability of intrusion for association words to improve the accuracy. In the test stage, the probability of intrusion logs and the probability of normal logs in a test log set are computed by Fisher's inverse chi-square classification algorithm based on the association word knowledge base respectively and intrusion logs are extracted from combining the results. Then, the intrusion logs are recommended to investigators. The proposed method uses a training method of clearly analyzing the meaning of data from an unstructured large log data. As the results, it complements the problem of reduction in accuracy caused by data ambiguity. In addition, the proposed method recommends intrusion logs by using Fisher's inverse chi-square classification algorithm. So, it reduces the rate of false positive(FP) and decreases in laborious effort to extract evidences manually.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.18 no.3
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• pp.187-192
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• 2015

Purpose: Measurement of serum ceruloplasmin level is the first step in screening for Wilson's disease (WD). Despite the rarity of WD in the general population, ceruloplasmin levels are routinely measured through hepatitis screening in both adults and children. Herein, we evaluated the diagnostic value of ceruloplasmin for the diagnosis of WD among children with hepatitis. Methods: We retrospectively reviewed data on serum ceruloplasmin levels measured as a serologic marker for patients with hepatitis at Asan Medical Center (Seoul, Korea) between from January 2004 to November 2013. The diagnosis of WD was confirmed by the identification of pathogenic variants in the ATP7B gene. To determine the diagnostic accuracy of ceruloplasmin, receiver operation characteristic (ROC) curves were constructed and the area under curve (AUC) were calculated. Results: Measurements of serum ceruloplasmin were performed in 2,834 children who had hepatitis. Among these, 181 (6.4%) children were diagnosed with WD. The sensitivity, specificity, and accuracy of a ceruloplasmin level of <20 mg/dL in the discrimination of WD were 93.4%, 84.2%, and 84.8%, respectively. In this study, 418 (14.7%) false-positive cases and 12 (0.4%) false-negative cases were noted. Using a ROC curve, a ceruloplasmin level of ${\leq}16.6mg/dL$ showed the highest AUC value (0.956) with a sensitivity of 91.2%, a specificity of 94.9%, and an accuracy of 94.7%. Conclusion: The measurement of serum ceruloplasmin was frequently used for the screening of WD in children, despite a low positive rate. The diagnostic value of ceruloplasmin may be strengthened by adopting a new lower cut-off level.

• The Korean Journal of Nuclear Medicine
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• v.26 no.2
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• pp.312-317
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• 1992

To evaluate the sensitivity and specificity of captopril renal scan for renovascular hypertension, we employed the captopril renal scan in conjunction with renal angiography in 81 patients, 159 kidneys, who were referred to evaluate the cause of hypertension. We defined the renovascular hypertension by the criteria of demonstration of renal artery stenosis by angiography, and improvement or cure of hypertension by revascularization. Visual and quantitative evaluation of $^{99m}Tc-DTPA$ renal scan was peformed pre and post captopril administration. The prevalence rate of renovascular hypertension was 40% in comparing with renal angiography, and 70% in confirmed cases. The causes of renovascular hypertension in 81 patients were Takayasu's arteritis, fibromuscular dysplasia, atherosclerosis, essential hypertension, chronic pyetonephritis etc. The sensitivity and specificity of captopril renal scan in comparing with renal angiography were 80%, 86.5%, respectively and also 84.2%, 72.6% in confirmed cases of renovascular hypertension, respectively. The causes of false negative cases were nonfunctioning kidney due to complete obstruction or long duration of disease in basal scan, segmental branch artery stenosis, unknown causes, and suspicious true negative cases without confirmation. The false positive cases were abdominal aortic stenosis or aneurysm, dehydration, unknown causes, and suspicious true positive cases. We conclude that captopril renal scintigraphy is highly sensitive, reasonably specific diagnostic method and comparable to other techniques very favorably.