• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.107-113
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• 2021

In this paper, we conducted an empirical study to investigate whether Android app descriptions provide enough permission usages for measuring app quality in terms of human writing and consistency between code and descriptions. Android app descriptions are analyzed for various purposes such as quality measurement, functionality recommendation, and malware detection. However, many app descriptions do not disclose permission usages, whether accidentally or on purpose. Most importantly, the previous studies could not precisely analyze app descriptions if permission usages cannot be completely introduced in app descriptions. To assess the consistency between permissions and app descriptions, we implemented a state-of-the-art method to predict Android permissions for 29,270 app descriptions. As a result, 25% of app descriptions may not contain any permission semantic, and 57% of app descriptions cannot accurately reflect permission usages.

• Journal of KIISE
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• v.43 no.3
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• pp.275-288
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• 2016

In this paper, we investigate the security issues of the Android platform which dominates the global market of smart mobile devices. The current permission model for Android security is not powerful and has two problems. One is the coarse-grained relationship between permissions and methods which require them. The other is that mobile users do not have rights to control the permissions of the application. To solve these problems, we propose MacDroid which can control the platform's resources for accessing installed applications. Users can control the application's behavior via MacDroid's policy. We have divided the permission set into method units. The results of the performance test using a pure Android platform show that our proposed scheme can improve security within a short time.

• The KIPS Transactions:PartC
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• v.18C no.3
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• pp.151-156
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• 2011

Google Android, which is one of the popular smart phone platforms, employs a security model based on application permissions. This model intends to reduce security threats by protecting inappropriate accesses to system resources from applications, but this model has a few problems. First, permission requested by an application cannot be granted selectively. Second, once the permission has been granted it is maintained until the application is uninstalled. Third, applications may acquire powerful permissions through user ID sharing without any notice to users. In order to overcome these limitations, we designed and implemented a flexible application permission management scheme. The goal of our scheme is to enhance security and user convenience while keeping compatibility to original platform. We also verified the operation of our scheme with real applications on Android emulator.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.617-623
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• 2018

This study focuses on detection of malicious code through AndroidManifest permissoion feature extracted based on Android static analysis. Features are built on the permissions of AndroidManifest, which can save resources and time for analysis. Malicious app detection model consisted of SVM (support vector machine), NB (Naive Bayes), Gradient Boosting Classifier (GBC) and Logistic Regression model which learned 1,500 normal apps and 500 malicious apps and 98% detection rate. In addition, malicious app family identification is implemented by multi-classifiers model using algorithm SVM, GPC (Gaussian Process Classifier) and GBC (Gradient Boosting Classifier). The learned family identification machine learning model identified 92% of malicious app families.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.91-99
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• 2016

A security system in Android OS adopts sandbox and an permission model. In particular, the permission model operates the confirmation of installation time and all-or-nothing policy. Accordingly, the Android OS requires a user agreement for permission when installing an application, however there is very low level of user awareness for the permission. In this paper, the current status of permission requirement within mobile apps will be discovered, and the key inspection list with an appropriate method, when a mobile service provider autonomously inspects the violation of least privilege around financial companies, and its usefulness will be explored.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2591-2611
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• 2020

Botnets have become one of the most significant threats to Internet-connected smartphones. A botnet is a combination of infected devices communicating through a command server under the control of botmaster for malicious purposes. Nowadays, the number and variety of botnets attacks have increased drastically, especially on the Android platform. Severe network disruptions through massive coordinated attacks result in large financial and ethical losses. The increase in the number of botnet attacks brings the challenges for detection of harmful software. This study proposes a smart framework for mobile botnet detection using static analysis. This technique combines permissions, activities, broadcast receivers, background services, API and uses the machine-learning algorithm to detect mobile botnets applications. The prototype was implemented and used to validate the performance, accuracy, and scalability of the proposed framework by evaluating 3000 android applications. The obtained results show the proposed framework obtained 98.20% accuracy with a low 0.1140 false-positive rate.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.389-396
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• 2022

Smartphones are growing more susceptible as technology develops because they contain sensitive data that offers a severe security risk if it falls into the wrong hands. The Android OS includes permissions as a crucial component for safeguarding user privacy and confidentiality. On the other hand, mobile malware continues to struggle with permission misuse. Although permission-based detection is frequently utilized, the significant false alarm rates brought on by the permission-based issue are thought to make it inadequate. The present detection method has a high incidence of false alarms, which reduces its ability to identify permission-based attacks. By using permission features with intent, this research attempted to improve permission-based detection. However, it creates an excessive number of features and increases the likelihood of false alarms. In order to generate the optimal number of features created and boost the quality of features chosen, this research developed an intersection feature approach. Performance was assessed using metrics including accuracy, TPR, TNR, and FPR. The most important characteristics were chosen using the Correlation Feature Selection, and the malicious program was categorized using SVM and naive Bayes. The Intersection Feature Technique, according to the findings, reduces characteristics from 486 to 17, has a 97 percent accuracy rate, and produces 0.1 percent false alarms.

• Journal of KIISE
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• v.42 no.12
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• pp.1611-1622
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• 2015

Although Android systems provide a permission-based access control mechanism and demand a user to decide whether to install an app based on its permission list, many users tend to ignore this phase. Thus, an improved method is necessary for users to intuitively make informed decisions when installing a new app. In this paper, with regard to the permission-based access control system, we present a novel approach based on a machine-learning technique in order to support a user decision-making on the fly. We apply the K-NN (K-Nearest Neighbors) classification algorithm with necessary weighted modifications for malicious app classification, and use 152 Android permissions as features. Our experiment shows a superior classification result (93.5% accuracy) compared to other previous work. We expect that our method can help users make informed decisions at the installation step.

• 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 Korea Society of Digital Industry and Information Management
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• v.11 no.2
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• pp.47-54
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• 2015

The popularity and adoption of smart-phones has greatly stimulated the spread of mobile malware, especially on the popular platforms such as Android. The fluidity of application markets complicate smart-phone security. There is a pressing need to develop effective solutions. Although recent efforts have shed light on particular security issues, there remains little insight into broader security characteristics of smart-phone application. Now, the analytical methods used mainly are the reverse engineering-based analysis and the sandbox-based analysis. Such methods are can be analyzed in detail. but, they take a lot of time and have a one-time payout. In this study, we develop a system to monitor that mobile application permissions at application update. We had to overcome a one-time analysis. This study is a service-based malware analysis, It will be based will be based on the mobile security study.