• ETRI Journal
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• 제35권2호
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• pp.259-269
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• 2013

Android has become the most popular platform for mobile devices. However, Android still has critical performance issues, such as "application not responding" errors and hiccups resulting from garbage collection. Many phone vendors have tried to resolve the problems by characterizing and improving the performance. However, there are few insightful performance analysis tools for the Android-based systems. This paper presents AndroScope, which is a performance analysis tool for both the Android platform (Dalvik virtual machine, core libraries, Android libraries, and even Linux kernels) and its applications. To the best of our knowledge, this is the first tool to collect and analyze performance data from all the software layers of the Android-based systems. AndroScope offers a trace mechanism to collect such deep and wide performance data as hardware performance counters, time, and memory usage. In addition, the tool includes TraceBridge, which is a middleware for the fast handling of mass logs. Moreover, AndroScope offers an integrated graphical user interface with the Android software development kit to display a great volume of the detailed performance data.

• 한국산학기술학회논문지
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• 제13권5호
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• pp.2338-2346
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• 2012

향후 모바일 어플리케이션 규모는 커질 것으로 예상되며 이에 따라 다른 모바일 어플리케이션과 또는 서버와의 결합도가 커질 것이다. 모바일 어플리케이션 규모의 증가는 가변성을 위한 예측 설계가 수반되어야 함을 의미한다. 현재 모바일 어플리케이션 변경이 발생할 경우 어플리케이션 전체를 재설치 해야 한다. 그러나 이러한 재설치는 결합도가 큰 어플리케이션인 경우 부작용(Side-Effect)이 발생할 가능성이 높다. 따라서 본 논문에서는 안드로이드 플랫폼 기반에서 어플리케이션 가변성에 대해 설계할 수 있는 기법을 제안한다. 이러한 모바일 가변성 기법은 선택 기법과 플러그인 기법으로 구분한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3723-3737
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• 2022

Google Play is one of the largest Android phone app markets and it contains both free and paid apps. It provides a variety of categories for every target user who has different needs and purposes. The customer's rate every product based on their experience of apps and based on the average rating the position of an app in these arch varies. Fraudulent behaviors emerge in those apps which incorporate search rank maltreatment and malware proliferation. To distinguish the fraudulent behavior, a novel framework is structured that finds and uses follows left behind by fraudsters, to identify both malware and applications exposed to the search rank fraud method. This strategy correlates survey exercises and remarkably joins identified review relations with semantic and behavioral signals produced from Google Play application information, to distinguish dubious applications. The proposed model accomplishes 90% precision in grouping gathered informational indexes of malware, fakes, and authentic apps. It finds many fraudulent applications that right now avoid Google Bouncers recognition technology. It also helped the discovery of fake reviews using the reviewer relationship amount of reviews which are forced as positive reviews for each reviewed Google play the android app.

• 디지털콘텐츠학회 논문지
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• 제14권3호
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• pp.381-390
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• 2013

휴대폰 사용자들 대부분이 스마트폰으로 옮겨가면서 사용자들은 이제 스마트폰으로 다양한 게임을 즐기고 있다. 그런데 현재 앱스토어에 기기의 성능을 고려한 2D 게임은 다양하게 올라오고 있지만 3D 게임의 수는 아직도 많이 부족한 것이 실정이다. 이는 3D 게임이 많은 컴퓨팅 성능을 필요로 하기 때문인데, 본 논문에서는 안드로이드에서 NDK를 이용하여 3D큐브 게임을 구현하였다. 기존 비주얼C++ 일리히트 3D 큐브 소스를 NDK를 이용해 안드로이드 시스템 라이브러리로 이식하고 편의 기능을 추가하는 방식으로 구현하였다. NDK를 이용했으므로 기존 C++ 코드의 대부분이 자바 코드로 변환되지 않고 그대로 실행될 수 있어서 실행 시험 결과 비교적 구형 스마트폰 상에서도 3D 그래픽 동작들이 무난하게 이루어짐을 확인할 수 있었다.

• 대한임베디드공학회논문지
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• 제11권6호
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• pp.335-341
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• 2016

As information technology advances rapidly, various smart devices are becoming an essential element in our lives. Smart devices are providing services to users through applications up on the operating system. Operating systems have a variety of rules, such as scheduling applications and controlling hardwares. Among those rules, it is significant to protect private information in the information-oriented society. Therefore, isolation task, that makes certain memory space separated for each application, should highly be guaranteed. However, modern operating system offers the function to access the memory space from other applications for the sake of debugging. If this ability is misused, private information can be leaked or modified. Even though the access authority to memory is strictly managed, there exist cases found exploited. In this paper, we analyze the problems of the function provided in the Android environment that is the most popular and opened operating system. Also, we discuss how to avoid such kind of problems and verify with experiments.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2010년도 추계학술발표대회
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• pp.502-505
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• 2010

본 논문에서는 스마트폰 환경에서 개인 사용자 편의 서비스 프로그램 개발의 기초가 되는 작업 계획 모델 및 실행 모델을 제시하고, 이 모델을 활용하여 Android 플랫폼에서 개발된 자동 작업 실행 체계인 Smart Script 시스템의 설계와 구현에 대해 소개한다.

• 한국콘텐츠학회논문지
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• 제16권5호
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• pp.13-20
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• 2016

소프트웨어는 대부분 바이너리 파일 포맷으로 배포되기 때문에 역공학 분석이 쉽지 않다. 그러나 안드로이드는 자바를 기반으로 하며 가상머신 위에서 동작한다. 따라서 안드로이드 역시 자바와 유사하게 역공학 도구에 의해서 쉽게 분석될 수 있다. 이 문제를 극복하기 위해서 다양한 난독화 기법이 제안되었다. 안드로이드 환경에서는 안드로이드 SDK에 포함되어 배포되는 난독화 도구인 프로가드(Proguard)가 가장 널리 사용된다. 프로가드는 자바 소스 코드를 역공학 분석으로부터 보호할 수 있다. 그러나 이미지, 사운드, 데이터베이스와 같은 리소스를 보호하는 기능은 가지고 있지 않다. 본 논문에서는 안드로이드 앱의 리소스를 보호할 수 있는 리소스 난독화 기법을 제안하고 구현하였다. 본 논문에서 제안하는 리소스 난독화 기법을 적용하면 효과적으로 리소스 도용을 예방할 수 있을 것으로 기대한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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• pp.4658-4679
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• 2017

Android is now the most popular smartphone platform and remains rapid growth. There are huge number of sensitive privacy information stored in Android devices. Kinds of methods have been proposed to detect Android malicious applications and protect the privacy information. In this work, we focus on extracting the fine-grained features to maximize the information of Android malware detection, and selecting the least joint features to minimize the number of features. Firstly, permissions and APIs, not only from Android permissions and SDK APIs but also from the developer-defined permissions and third-party library APIs, are extracted as features from the decompiled source codes. Secondly, feature selection methods, including information gain (IG), regularization and particle swarm optimization (PSO) algorithms, are used to analyze and utilize the correlation between the features to eliminate the redundant data, reduce the feature dimension and mine the useful joint features. Furthermore, regularization and PSO are integrated to create a new joint feature mining method. Experiment results show that the joint feature mining method can utilize the advantages of regularization and PSO, and ensure good performance and efficiency for Android malware detection.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.202-209
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• 2023

Android malware is now on the rise, because of the rising interest in the Android operating system. Machine learning models may be used to classify unknown Android malware utilizing characteristics gathered from the dynamic and static analysis of an Android applications. Anti-virus software simply searches for the signs of the virus instance in a specific programme to detect it while scanning. Anti-virus software that competes with it keeps these in large databases and examines each file for all existing virus and malware signatures. The proposed model aims to provide a machine learning method that depend on the malware detection method for Android inability to detect malware apps and improve phone users' security and privacy. This system tracks numerous permission-based characteristics and events collected from Android apps and analyses them using a classifier model to determine whether the program is good ware or malware. This method used the machine learning techniques KNN-SVM, DBN, and GRU in which help to find the accuracy which gives the different values like KNN gives 87.20 percents accuracy, SVM gives 91.40 accuracy, Naive Bayes gives 85.10 and DBN-GRU Gives 97.90. Furthermore, in this paper, we simply employ standard machine learning techniques; but, in future work, we will attempt to improve those machine learning algorithms in order to develop a better detection algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권9호
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• pp.3357-3376
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• 2015

Wireless sensor network is an important research topic that has attracted a lot of attention in recent years. However, most of the interest has focused on wireless sensor network to gather scalar data such as temperature, humidity and vibration. Scalar data are insufficient for diverse applications such as video surveillance, target recognition and traffic monitoring. However, if we use camera sensors in wireless sensor network to collect video data which are vast in information, they can provide important visual information. Video sensor networks continue to gain interest due to their ability to collect video information for a wide range of applications in the past few years. However, how to efficiently store the massive data that reflect environmental state of different times in video sensor network and how to quickly search interested information from them are challenging issues in current research, especially when the sensor network environment is complicated. Therefore, in this paper, we propose a fast algorithm for extracting key frames from video and describe the design and implementation of key frame extraction and sharing in Android for wireless video sensor network.