• Journal of Internet Technology
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• v.22 no.2
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• pp.423-439
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• 2021

Android is one of the most popular platforms for the mobile and Internet of Things (IoT) devices. This popularity has made Android-based devices a valuable target of malicious apps. Thus, it is essential to devise automatic and portable malware detection approaches for the Android platform. There are many studies on detecting mobile malware using machine learning techniques. In these studies, however, the dataset is imbalanced or is not large enough to generalize the machine learning model, or the dimensionality of features is too high to apply nonlinear classifiers. In this article, we propose a machine learning-based Android malware detection scheme that uses API calls and permissions as features. To restrict the dimensionality of features, we propose minimal domain knowledge-based and Gini importance-based feature selection. We construct large and balanced real-world datasets to build a generalized and non-skewed model and verify our model through experiments. We achieve 96.51% classification accuracy using Random Forest classifier with low overhead. In addition, we also provide an analysis on falsely classified samples in detail. The analysis results show that API hiding can degrade the performance of API call information-based malware detection systems.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.4
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• pp.463-469
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• 2014

We propose an interworking architecture between a smart TV and a smart phone. This architecture provides control mechanism for the newly adopted App on Smart-TV with smart phone. The general method connecting between a smart phone and a mobile AP is very carefully complex and troublesome procedure. The mobile AP transfers data from a smart phone to a smart TV after getting the connection. Connecting procedure between a smart phone and a mobile AP is therefore necessary operation. We worked out the new procedure and control method that acquires some information from Near Field Communication or Quick Reference codes for connecting between a smart TV and a smart phone. Our proposed architecture was confirmed practical possibility as user-oriented automatic connection and simple manipulation for control a kiosk or various display equipment.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.59-66
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• 2022

In this paper, by applying IoT technology to the Ultimate game, which is one of the games using Flyingdisc, the process of the game is traced based on the players and the flyingdisc, and a comprehensive relationship analysis between players is performed on the results of the game. A WiFi module with built-in GPS is attached in the players and flyingdisc. The player's ID, latitude/longitude values received from GPS and time are stored in the database in realtime during the game. Process informations of the game is also stored in the database at the same time using mobile Ultimate game App. Based on this informations after the game is over, we developed a system that can perform comprehensive analysis of the game contents. By using the informations stored in the database, the player-based game process and the flyingdisc-based scoring process are visualized in the virtual playground. Various game result informations for players are graphically analyzed using Python.

• Journal of the Korea Institute of Construction Safety
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• v.5 no.1
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• pp.1-8
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• 2023

The purpose of this study is to suggest a way to solve the excessive paperwork of safety managers in domestic construction sites, and to suggest a work efficiency plan that can shorten the time required to prevent safety accidents. First, a function to automatically generate a safety document and find the necessary data is applied using the RPA program. The second is document creation using mobile devices. After safety training, use the Moleil app to keep the training log. Third, to prevent omission of essential safety and health documents, the automatic warning function is activated according to the RPA submission time and sent to the person in charge by e-mail or text. Fourth, the function to find the latest data with high accuracy and speed through 'Google Cloud Search', a search function, was applied.

• Journal of Veterinary Clinics
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• v.41 no.2
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• pp.71-78
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• 2024

Changes in elimination behavior, including urination and defecation, are common clinical signs of numerous disorders in cats. Therefore, this study attempted to automatically measure the elimination behavior of cats using the litter box and develop an early warning system for the guardian in case of abnormalities. To construct an early warning system for abnormal changes through cat elimination behavior, it consisted of a litter box, an automatic sensor for data collection and data wifi transmission, a server for data analysis, and a mobile phone app for result transmission and early warning. To establish the reference interval (RI), the elimination behavior was monitored for more than 2 weeks using a motion sensor within a litter box in 37 healthy cats and 19 diseased cats. The data were expressed as daily total visits, daily total stay duration, average stay duration per elimination, weekly total visits, and weekly total stay duration. Healthy cats showed median daily total visits of 3 times/day (RI 1.0-7.0) and daily total stay duration of 192 s/day (RI 8.0-452.0). For weekly data, the median total visits were 20 times/week (RI 3.0-35.25) and the median total stay duration was 1,147 s/week (RI 80.0-2,249.5). The average stay duration per elimination was 59 s/elimination (RI 4.67-132.0). Diseased cats showed more frequent elimination behavior than healthy cats (p < 0.001). Otherwise, for each elimination, diseased cats had shorter stay durations than healthy cats (p < 0.001). This study established the RIs of elimination behavior parameters (frequency and duration) in healthy cats. The present study might help guardians and veterinarians detect changes in elimination behaviors in diseased cats at an early stage.