Extending Android Security Enforcement with a Security Distance Model

Tang, Wei; Jin, Guang; He, Jingsong; Jiang, Xianliang

doi:10.1109/itap.2011.6006288

Cited by 24 publications

(10 citation statements)

References 5 publications

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“…Tang, Wei et al [35] proposed a Security Distance Model for mitigation of Android malware. Security Distance Model is based on the concept that not a single permission is enough for an application to threaten the security of Android devices.…”

Section: ) Permission Based Analysismentioning

confidence: 99%

Android Malware Detection & Protection: A Survey

Arshad¹,

Shah²,

Khan³

et al. 2016

ijacsa

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Abstract-Android has become the most popular smartphone operating system. This rapidly increasing adoption of Android has resulted in significant increase in the number of malwares when compared with previous years. There exist lots of antimalware programs which are designed to effectively protect the users' sensitive data in mobile systems from such attacks. In this paper, our contribution is twofold. Firstly, we have analyzed the Android malwares and their penetration techniques used for attacking the systems and antivirus programs that act against malwares to protect Android systems. We categorize many of the most recent antimalware techniques on the basis of their detection methods. We aim to provide an easy and concise view of the malware detection and protection mechanisms and deduce their benefits and limitations. Secondly, we have forecast Android market trends for the year up to 2018 and provide a unique hybrid security solution and take into account both the static and dynamic analysis an android application.

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Section: ) Permission Based Analysismentioning

confidence: 99%

Android Malware Detection & Protection: A Survey

Arshad¹,

Shah²,

Khan³

et al. 2016

ijacsa

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“…The model is looking for differences between permissions and combination of permissions with a security problem. The higher the value of threat point indicated an application has a large security threat, otherwise declared as safe from malicious apps [9].…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Study for Mobile Android Malware Detection

2015

International Academy of Engineers (IA-E) June 10-11, 2015 Antalya (Turkey)

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Abstract-Android Malware is a threat that needs to be considered by the Android OS user because it can interfere with the performance of the smartphone being used. Many applications that are scattered on the Google Store is a point of weakness for the user to determine which applications containing malware or not. This comprehesive study can explain about many applications that are scattered on the Google Store is a point of weakness for the user to determine which applications containing malware or not using several methods and display the comparison results between all previous study in the review of Android Mobile Malware. Till now there the optimal method suggested solution that could be considered as standard mechanism for Android Malware Detection is Bayesian classification algorithm based on the methods of previous research used in the detection of Android Malware. For this reason we intended to make our work as a survey paper to make it easier for Android user to determine installed application contains malicious program or not.

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“…One of the most commonly used approaches is a security-based permission model. Tang et al [6] highlights that Android Security System and treatment are too weak and proposed ASESD to prevent malware. Ongtang et al [7] proposes the Saint framework, which grants permissions policies to overcome Android security weaknesses.…”

Section: Related Workmentioning

confidence: 99%

“…For example, if an application has the READ_PHONE_STATE and INTERNET permissions, that app can be used to get phone numbers, IMEI, user location etc. from the phone and can transmit the information to any Internet server [6]. Any application can also download and/or upload any kind of file in the background with appropriate permissions.…”

Section: A Android Osmentioning

confidence: 99%