• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.17-22
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• 2021

The attack trend on end-users via mobile devices is increasing in both the danger level and the number of attacks. Especially, mobile devices using the Android operating system are being recognized as increasingly being exploited and attacked strongly. In addition, one of the recent attack methods on the Android operating system is to take advantage of Android Package Kit (APK) files. Therefore, the problem of early detecting and warning attacks on mobile devices using the Android operating system through the APK file is very necessary today. This paper proposes to use the method of analyzing abnormal behavior of APK files and use it as a basis to conclude about signs of malware attacking the Android operating system. In order to achieve this purpose, we propose 2 main tasks: i) analyzing and extracting abnormal behavior of APK files; ii) detecting malware in APK files based on behavior analysis techniques using machine learning or deep learning algorithms. The difference between our research and other related studies is that instead of focusing on analyzing and extracting typical features of APK files, we will try to analyze and enumerate all the features of the APK file as the basis for classifying malicious APK files and clean APK files.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2188-2203
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• 2021

With the rapid development of mobile Internet, smart phones have been widely popularized, among which Android platform dominates. Due to it is open source, malware on the Android platform is rampant. In order to improve the efficiency of malware detection, this paper proposes deep learning Android malicious detection system based on behavior features. First of all, the detection system adopts the static analysis method to extract different types of behavior features from Android applications, and extract sensitive behavior features through Term frequency-inverse Document Frequency algorithm for each extracted behavior feature to construct detection features through unified abstract expression. Secondly, Long Short-Term Memory neural network model is established to select and learn from the extracted attributes and the learned attributes are used to detect Android malicious applications, Analysis and further optimization of the application behavior parameters, so as to build a deep learning Android malicious detection method based on feature analysis. We use different types of features to evaluate our method and compare it with various machine learning-based methods. Study shows that it outperforms most existing machine learning based approaches and detects 95.31% of the malware.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.213-215
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• 2012

The current Navigation System for the Visually Impaired Person has a short and limited communication distance and can't receive enough information from Visually Impaired Person to assist directly. In addition, because the path is dangerous and incomplete for the Visually Impaired Person, moving with White Stick is still inconvenient and dangerous. To solve this problem we implement communication that can send and receive video, voice, location information between the Visually Impaired Person's Android platform and assistant's PC.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.1
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• pp.67-80
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• 2012

Recently as the number of smartphone user is growing rapidly, android is also getting more interest in digital forensic. However, there is not enough research on digital data acquisition and analysis based on android platform's unique characteristics so far. Android system stores all the related recent systemwide logs from the system components to applications in volatile memory, and therefore, the logs can potentially serve as important evidences. In this paper, we propose a digital data acquisition and analysis system for android which extracts meaningful information based on the correlation of android logs and user activities from a device at runtime. We also present an efficient search scheme to facilitate realtime analysis on site. Finally, we demonstrate how the proposed system can be used to reconstruct the sequence of user activities in a more intuitive manner, and show that the proposed search scheme can reduce overall search and analysis time approximately 10 times shorter than the normal regular search method.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06d
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• pp.30-31
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• 2012

본 논문에서는 Android System 핵심 부분을 기존의 System Server 에 Application Server 를 더하여 2개의 서버로 재구성하는 방안을 논의한다. Application Server 는 DalvikVM 과 Application 저장/스케줄링 프로세스로 구성한다. Application Server 가 모든 사용자의 Application 을 저장하고 스케줄링하고 DalvikVM에 할당하여 실행시키도록 한다. 이 방법은 사용자 입장에서는 단순화된 Android System 구조로 인하여 응용의 개발이 용이해지고 응용의 반응도 빠르게 된다. 시스템 입장에서도 구조가 단순해져서 실행 효율이 좋아지고 메모리 사용 효율도 좋아진다.

• Journal of Internet Computing and Services
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• v.13 no.1
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• pp.135-140
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• 2012

As the recent advanced in BIO signal measurement technology, our computing platform is rapidly shifting from desktop PCs to Embedded System. Therefore, In this paper introduces an implementation of the same precision as a hospitan ECG system on the Android. The most important fact of the hospital system is connectivity among the PC such as separate means of communication, we can eliminate the separate means of communication through the Porting Embedded System on Android that can be receive ECG signal directly. We also implementation ECG App on Android that can analyze and show the data result directly.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.8
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• pp.3005-3011
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• 2010

As soon as high-performanced smart phones is rapidly emerging in recent, its security problems come to the front. Especially in case of an open platform, it is easy to be a target of virus. Many security solution industries such as Symantec and Ahnlab are developing a mobile security system, but they have not yet a commercial product. We developed the effective security function of Android system based on SELinux to solve this problem, and verified its performance by applying the user applications developed to S3C6410 board.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.475-484
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• 2021

The Android OS has quickly established itself with the smartphone market and is being applied in a variety of ways such as navigation and refrigerator panels. Existing terminals can implement services that require high precision while using RTOS that requires accurate execution time, but Android OS has a disadvantage in that it cannot provide high-precision real-time performance. In this paper, we propose a real-time communication method that operates in the Android OS. Real-time communication reduces the load using the UDP protocol, and configures the real-time operating system time tick through a high-precision timer in the kernel area. An Android application level library was created to implement real-time communication and compared with the existing Android library for real-time performance verification.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.147-153
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• 2014

In this paper, we propose smart emotional lighting control system based on the android platform. The proposed smart emotional lighting control system be configure android platform and sensibility lighting equipment, expansion device, zigbee module. Smart emotional lighting control system based on the android platform is automatic control possible using the illumination sensor, and by selecting the desired lighting partial control can be designed. The experimental results of the proposed smart emotional lighting control sensitivity than conventional lighting control system decreased the power consumption and efficient lighting control was possible. Office acts will be suitable conditions to control the color and brightness, so they can be controlled from the improves concentration and ability to work.