An Android malware detection method based on AndroidManifest file

Li, Xiang; Liu, Jianyi; Huo, Yanyu; Zhang, Ru; Yao, Yuangang

doi:10.1109/ccis.2016.7790261

Cited by 26 publications

(15 citation statements)

References 5 publications

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“…[157] refers to the experience of Arp et al [88] in the development of Drebin. [148] Information gain (Mutual information), Chi-square statistics (CS), Fisher score (FS) [149] Information gain (Mutual information), Dependence measure [102] Information gain (Mutual information), Dependence measure [150] TF-IDF, cosine similarity [151] Sequential forward selection (SFS) [107] Fisher score (FS) [152] Dependence measure [153] Pearson correlation coefficient [154] Chi-square statistics (CS) [155] Relief algorithm [156] Genetic search (GS) [157] The selection of static features refers to the experience of Arp et al in the Drebin project.…”

Section: Data Transformationmentioning

confidence: 99%

A Review of Android Malware Detection Approaches Based on Machine Learning

et al. 2020

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Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the problem of Android malware detection and have put forward theories and methods from different perspectives. Existing research suggests that machine learning is an effective and promising way to detect Android malware. Notwithstanding, there exist reviews that have surveyed different issues related to Android malware detection based on machine learning. We believe our work complements the previous reviews by surveying a wider range of aspects of the topic. This paper presents a comprehensive survey of Android malware detection approaches based on machine learning. We briefly introduce some background on Android applications, including the Android system architecture, security mechanisms, and classification of Android malware. Then, taking machine learning as the focus, we analyze and summarize the research status from key perspectives such as sample acquisition, data preprocessing, feature selection, machine learning models, algorithms, and the evaluation of detection effectiveness. Finally, we assess the future prospects for research into Android malware detection based on machine learning. This review will help academics gain a full picture of Android malware detection based on machine learning. It could then serve as a basis for subsequent researchers to start new work and help to guide research in the field more generally.

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Section: Data Transformationmentioning

confidence: 99%

A Review of Android Malware Detection Approaches Based on Machine Learning

et al. 2020

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“…The study, "Mobil Bankacılıkta Güvenlik Sorunlarının Analizi" [15] mobile application security methods are described and presented. In the studies, "Android Kötücül Yazılım Tespit Sistemleri İncelemesi" [16], "An Android Malware Detection Method Based on AndroidManifest File" [17] "Permission-Based Android Malware Detection" [18] and "MAMA: Manifest Analysis for Malware Detection in Android" [19], AndroidManifest.xml file has been seen that how important it is for application security. As seen in the literature, if there is not enough security on mobile devices, personal information of the users can be stolen, their privacy can be violated and the applications of the users can be disabled.…”

Section: Introductionmentioning

confidence: 99%

A Practical Approach to Android Mobile Application Security

Taşdemir¹,

Çalışkan²

2018

ijamec

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In parallel to rapid developments in computer technology, the number of mobile applications developed for the devices also increases. Mobile applications make life easier, but also bring some risks. These applications may create some weaknesses due to mistakes in the app development or use phase. In this study, a sample security test was performed for mobile application security awareness. This paper related to phising attacks to Android mobile users and data storage security on Android device. The sample mobile application has been decompiled. The malicious code was injected into the sample app. After the code was injected into the sample banking application developed, the user interface was modified. In addition, when the application is open, the user's credit card information is requested. After the user fills information, the credit card information is sent to a different phone number (attacker's phone number) through an SMS. The mobile user is at risk of stealing sensitive information. This study also shows that the data stored in the device can be accessed through the Android Debug Bridge (ADB) shell commands. As a result, this paper shows that the application developer should be more careful during the development phase and the device user should be more careful during the use phase.

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“…In order to address this question, the previous researchers find that there are two techniques for the unknown Android malware analysis: static analysis and dynamic analysis [2]. Static analysis, occurs before the Android application is installed, is a technique based on checking the contents of the APK [3] by means of reverse engineering.…”

Section: Introductionmentioning

confidence: 99%

An Android malware detection system based on machine learning

Wen¹,

Yu²

2017

AIP Conference Proceedings

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Abstract. The Android smartphone, with its open source character and excellent performance, has attracted many users. However, the convenience of the Android platform also has motivated the development of malware. The traditional method which detects the malware based on the signature is unable to detect unknown applications. The article proposes a machine learning-based lightweight system that is capable of identifying malware on Android devices. In this system we extract features based on the static analysis and the dynamitic analysis, then a new feature selection approach based on principle component analysis (PCA) and relief are presented in the article to decrease the dimensions of the features. After that, a model will be constructed with support vector machine (SVM) for classification. Experimental results show that our system provides an effective method in Android malware detection.

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An Android malware detection method based on AndroidManifest file

Cited by 26 publications

References 5 publications

A Review of Android Malware Detection Approaches Based on Machine Learning

A Review of Android Malware Detection Approaches Based on Machine Learning

A Practical Approach to Android Mobile Application Security

An Android malware detection system based on machine learning

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