• ETRI Journal
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• v.35 no.2
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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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.5
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• pp.2338-2346
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• 2012

According to the size of mobile applications has been expanded, the coupling of among mobile applications or servers also will be growing. The growth of mobile application's size means that predicting design for variability should be involved. If mobile application's change is occurred, application should be reinstalled totally. However this reinstallation can raise side-effects in case of high-coupling applications. Therefore, this paper proposes a technique of designing variability for mobile applications in android platform. Proposed technique is separated into selection technique and plug-in technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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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.

• Journal of Digital Contents Society
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• v.14 no.3
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• pp.381-390
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• 2013

Almost all the mobile phone users already moved or are now moving away to smartphones for their various applications like games. If we are to speak about game applications, due to the performance limits of smartphones, 2D games are predominant over 3D games in every app. store. In this paper, we implement a 3D cube game application by porting an existing visual c++ irrlicht cube application to android platform library using the android Native Development Kit. After the porting is done, we add a few new features for more fun. Because the android NDK makes the existing C/C++ codes run directly on the android operating systems, we found by real execution tests that our 3D cube app. is well executed on a low-end android smartphone without any performance problem.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.6
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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.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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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.

• The Journal of the Korea Contents Association
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• v.16 no.5
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• pp.13-20
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• 2016

Most software is distributed as a binary file format, so reverse engineering is not easy. But Android is based on the Java and running on virtual machine. So, Android applications can be analyzed by reverse engineering tools. To overcome this problem, various obfuscation techniques are developed. In android environment, the Proguard is most widely used because it is included in the Android SDK distribution package. The Proguard can protect the Java source code from reverse engineering analysis. But it has no function to protect resources like images, sounds and databases. In this paper, we proposed and implemented resource obfuscation framework to protect resources of android application. We expect that this framework can protect android resources effectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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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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• v.23 no.7
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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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• v.9 no.9
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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.