• Convergence Security Journal
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• v.23 no.3
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• pp.13-20
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• 2023

This study conducted a digital forensic analysis of Signal and Telegram, two secure messengers widely used in the Android environment. As mobile messengers currently play an important role in daily life, data management and security within these apps have become very important issues. Signal and Telegram, among others, are secure messengers that are highly reliable among users, and they safely protect users' personal information based on encryption technology. However, much research is still needed on how to analyze these encrypted data. In order to solve these problems, in this study, an in-depth analysis was conducted on the message encryption of Signal and Telegram and the database structure and encryption method in Android devices. In the case of Signal, we were able to successfully decrypt encrypted messages that are difficult to access from the outside due to complex algorithms and confirm the contents. In addition, the database structure of the two messenger apps was analyzed in detail and the information was organized into a folder structure and file format that could be used at any time. It is expected that more accurate and detailed digital forensic analysis will be possible in the future by applying more advanced technology and methodology based on the analyzed information. It is expected that this research will help increase understanding of secure messengers such as Signal and Telegram, which will open up possibilities for use in various aspects such as personal information protection and crime prevention.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.01a
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• pp.9-12
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• 2013

리눅스 기반 안드로이드 단말에서는 CFS(Completely Fair scheduler)가 사용되고 있다. 그리고 CFS는 태스크의 nice값 조절을 통해서 응용프로그램의 CPU 점유율을 제어할 수 있다. 하지만 안드로이드를 업그레이드할 때마다 수많은 태스크의 nice값을 적절하게 맞추는 일은 매우 어려운 일이다. 이러한 문제를 해결하기 위하여 안드로이드 단말은 리눅스의 cgroup(control group)을 사용하여 태스크들을 그룹으로 나눈다. 고성능과 빠른 응답 특성이 필요한 태스크들을 apps 그룹에 할당하여 높은 CPU 점유율을 보장하고, 그렇지 않은 태스크들을 background 그룹에 할당한다. 하지만 안드로이드의 버전이 업그레이드 되면서 각 그룹에 속한 태스크들에도 변화가 생긴다. 그 결과 동일하게 제작된 태스크들의 CPU 점유율이 달라지게 되고 예기치 못한 성능 저하가 발생할 수 있다. 본 연구에서는 안드로이드 버전 향상에 따라 동종 태스크들이 이전 버전에서보다 성능이 하락하는 현상의 원인을 파악하였다. 아울러 분석과 실험을 통하여 태스크의 nice 값보다 그룹 스케줄링 메커니즘이 어떻게 태스크의 CPU 점유율을 결정 짓는지 규명하였다.

• Journal of Korea Game Society
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• v.15 no.2
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• pp.73-82
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• 2015

This study makes a proposal about Flash mobile game applications using Adobe AIR. In developing a mobile game, the developer programs in Flash ActionScript, and distributes and publishes the program using Adobe AIR so that the game can be played on an Android mobile device. In order to run the game, the player downloads and installs Android Adobe AIR onto the mobile device and sets up the published app. This study designed and implemented a mobile game application and showed that a mobile game is executed on a smart phone. This outcome may be applicable to various genres of apps. Moreover, this study analyzed the trends of mobile games, focusing on their genres and characteristics, and according to the results, most of them were mobile games using Kakao Talk. The analysis results also showed that the popularity ranking of games varied little among sites.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.79-80
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• 2018

안드로이드 운영체제는 앱을 설치하거나 실행할 때 사용자가 해당 앱이 요청하는 권한들을 승인하도록 하고 있으나, 일반적인 사용자들은 이를 주의 깊게 확인하지 않고 승인하는 경우가 많으며, 과도한 권한들을 요구하는 앱의 실행은 프라이버시 침해 문제로 이어질 수 있다. 본 논문에서는 제공하는 기능들에 비해 과도한 권한들을 요구하는 안드로이드 앱들을 탐지하는 모델을 제안한다. 먼저 손전등, 다이어리, 지불(페이) 및 채팅 앱 207개를 대상으로 요구하는 권한들을 조사하여 정리하였다. 조사 결과를 기준으로 설치 또는 실행하려는 앱이 어느 정도의 권한들을 요구하는지 가늠할 수 있다. 설치된 앱들의 요구 권한들을 조회할 수 있는 앱 프로토타입을 개발하였으며, 향후 모델의 구체화 및 검증을 거쳐, 프로토타입에 적용할 계획이다.

• Journal of the Korea Convergence Society
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• v.7 no.2
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• pp.25-31
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• 2016

In the study, a convergence model of a mobile-based English speaking test was developed. Based on the needs analysis from Korean college students, a speaking test App for iOS and Android mobile devices was developed and its test validity was examined. To develop the test, test items, scoring features and scales were developed. The result of the needs analysis survey showed that 69 out of 87 (79.3%) owned Android phones whereas only 17 (19.5%) held iPhones. Based on the result, an English speaking test App was developed. The technologies used for the test included HTML5, the Javasciript bridge technology, Node and Express, and Mongo DB. In the experiment, five examinees took the test. The test validity was obtained in that all the functions of the test were successfully operated and some feedback was gathered.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.433-441
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• 2021

Android applications are released across various categories, including productivity apps and games, and users are exposed to various applications and even malware depending on their usage patterns. On the other hand, most analysis engines train using existing datasets and do not reflect user patterns even if periodic updates are made. Thus, the detection rate for known malware is high, while types of malware such as adware are difficult to detect. In addition, existing engines incur increased service provider costs due to the cost of server farm, and the user layer suffers from problems where availability and real-timeness are not guaranteed. To address these problems, we propose an analysis system that performs on-device malware detection through transfer learning, which requires only one-time communication with the server. In addition, The system has a complete process on the device, including decompiler, which can distribute the load of the server system. As an evaluation result, it shows 90.3% accuracy without transfer learning, while the model transferred with adware catergories shows 95.1% of accuracy, which is 4.8% higher compare to original model.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.27-35
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• 2018

This paper is a study to classify malicious applications in Android environment. And studying the threat and behavioral analysis of malicious Android applications. In addition, malicious apps classified by machine learning were performed as experiments. Android behavior analysis can use dynamic analysis tools. Through this tool, API Calls, Runtime Log, System Resource, and Network information for the application can be extracted. We redefined the properties extracted for machine learning and evaluated the results of machine learning classification by verifying between the overall features and the main features. The results show that key features have been improved by 1~4% over the full feature set. Especially, SVM classifier improved by 10%. From these results, we found that the application of the key features as a key feature was more effective in the performance of the classification algorithm than in the use of the overall features. It was also identified as important to select meaningful features from the data sets.

• Convergence Security Journal
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• v.12 no.1
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• pp.49-54
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• 2012

Special applications designed for smart phone, so called "Apps" are rapidly emerging as unique and effective sources of environmental monitoring tools. Using the advantages of Information and Communication Technology (ICT), this paper propose an application that provides Indoor Gas Monitoring System. In this paper, use four wireless gas sensor modules to acquire sensors data wirelessly coupled with the advantages of existing portable smart device based on Android platform to display the real-time data from the sensor modules. Additionally, this paper adapts a simple gas classification algorithm to inform in-door Gas for users real-time based.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.397-400
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• 2012

To activate the use of the mobile Internet, its service must be changed itself, as well as improvement of its use environment, and the changing direction must orient 'SMART SERVICE', which it provides in the fastest, easiest, most comfortable way by selecting the core information users want most. This paper suggests that the users of smartphones access diverse contents out of the mobile apps anytime and anywhere and use them conveniently, by activating social communication among the users of school information through the mobile app development suitable for Android OS.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.499-502
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• 2017

스마트폰 보급률이 올라감에 따라 마켓에 등록되는 앱 또한 폭발적으로 증가하고 있다. 이에 따라 앱을 대상으로 하는 다양한 연구가 진행되고 있어 연구자에게 다양한 앱을 모으는 작업이 중요해지고 있다. 이 논문에서는 안드로이드 앱을 효율적으로 저장하는 저장소를 제안하고자 한다. 제안 시스템은 임의의 앱을 입력으로 받아 자동으로 분류 후 기존 앱과 차이점만을 저장하는 것으로 용량 절감 효과가 있다. 이 논문에서는 실험을 통해 제안 시스템이 앱을 자동으로 분류함을 보였으며, 35개 앱(7종의 서로 다른 앱 5개)을 대상으로 실험한 경우 약 37~40%의 용량 절감 효과가 있는 것으로 나타났다.