An integrated static detection and analysis framework for android

Song, Jun; Han, Chunling; Wang, Qing; Zhao, Jian; Ranjan, Rajiv; Wang, Lizhe

doi:10.1016/j.pmcj.2016.03.003

Cited by 50 publications

(26 citation statements)

References 18 publications

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“…In the last stage, an online passive aggressive classifier is used and trained to detect malicious software with these vectors [13]. In the ASE study, an integrated static detection system with four filtering layers was proposed, including MD5 (Message Digest 5) detection of characteristic values, detection of combination of malicious permissions, detection of hazardous permissions and detection of hazardous intent [14]. Wang et al proposed a system to manage a large application market effectively and efficiently in order to categorize malicious and benign applications [15].…”

Section: Static Analysis Methodsmentioning

confidence: 99%

Web-Based Android Malicious Software Detection and Classification System

Doğru¹,

Kiraz²

2018

Applied Sciences

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Android is the most used operating system (OS) by mobile devices. Since applications uploaded to Google Play and other stores are not analyzed comprehensively, it is not known whether the applications are malicious software or not. Therefore, there is an urgent need to analyze these applications regarding malicious software. Moreover, mobile devices have limited resources to analyze the applications. In this study, a malicious detection system named “Web-Based Android Malicious Software Detection and Classification System” was developed. The system is based on client-server architecture, static analysis and web-scraping methods. The proposed system overcomes the resource restriction issue, as well as providing third-party service support by means of client-server architecture. Based on the performance evaluation conducted in this research, the developed system’s success rate is 97.62% on benign and malicious datasets.

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Section: Static Analysis Methodsmentioning

confidence: 99%

Web-Based Android Malicious Software Detection and Classification System

Doğru¹,

Kiraz²

2018

Applied Sciences

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“…[22]. Static detection framework is beneficial in efficiency, granularity, layers, as well as correctness [23]. Sandbox an emulator records and analyzes the app's behavior used to modify the short message (SM) sending function of the emulator, in order to record the SM content and receiver when the function is performed [24].…”

Section: Static Analysismentioning

confidence: 99%

A Survey on Android Malware Detection Techniques

Riasat¹,

Sakeena²,

Wang³

et al. 2017

dtcse

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Abstract. Now-a-days widely applicable smart phone application adoption and rapid growth of contextually sensitive nature of smartphone devices has led to a renaissance in mobile application services and increased concerns over smartphone malware. Most of the smart phone users are connected to internet for various purposes. Even after the advanced technology used in the smartphones still the users are remaining unprotected from malware attacks. To overcome these issues and to keep the smartphones safe a number of malicious applications, malware threat and adwares for mobile phones is expected to increase with the functionality enhancement of smart mobile phone. Android Operating Systems are most commonly used systems in the smartphones. Many applications are available in android play store and it is very difficult to distinguish and to discriminate between clean and malicious applications. We have briefly discussed different methods for detecting android malwares in this review and present a clear picture about these detection tools in the smartphone applications.

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“…In addition they also analyse ransomware samples, obtaining a precision of 0.961 with LADTree classification algorithm using the Structural Entropy method, and a precision of 0.824 with J48 algorithm using the HMM one in ransomware identification. Song et al (Song et al, 2016) propose a framework to statically detect Android malware, consisting of four layers of filtering mechanisms: the message digest values, the combination of malicious permissions, the dangerous permissions, and the dangerous intention. As additional contribute, they propose a novel threat degree threshold model of dangerous permissions on malware detection.…”

Section: Related Workmentioning

confidence: 99%

Identifying Mobile Repackaged Applications through Formal Methods

Martinelli

Mercaldo

Nardone

et al. 2017

Proceedings of the 3rd International Conference on Information Systems Security and Privacy

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Smartphones and tablets are rapidly become indispensable in every day activities. Android has become the most popular operating system for mobile environments in the world. These devices, owing to the open nature of Android, are continuously exposed to attacks, mostly to data exfiltration and monetary fraud. There are many techniques to embed the bad code, i.e. the instructions able to perform a malicious behaviour, into a legitimate application: the most diffused one is the so-called repackaged, that consists of reverse engineer the application in order to embed the malicious code and then (re)distribute them in the official and/or third party markets. In this paper we propose a technique to localize malicious payload of GinMaster family, one of the most representative repackaged trojan in Android environment. We obtain encouraging results, achieving an accuracy equal to 0.9.

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An integrated static detection and analysis framework for android

Cited by 50 publications

References 18 publications

Web-Based Android Malicious Software Detection and Classification System

Web-Based Android Malicious Software Detection and Classification System

A Survey on Android Malware Detection Techniques

Identifying Mobile Repackaged Applications through Formal Methods

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