APK حسابرس: سیستم تشخیص نرم افزارهای مخرب آندرویدی مبتنی بر اجازه
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|40525||2015||14 صفحه PDF||سفارش دهید||6735 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Digital Investigation, Volume 13, June 2015, Pages 1–14
Android operating system has the highest market share in 2014; making it the most widely used mobile operating system in the world. This fact makes Android users the biggest target group for malware developers. Trend analyses show large increase in mobile malware targeting the Android platform. Android's security mechanism is based on an instrument that informs users about which permissions the application needs to be granted before installing them. This permission system provides an overview of the application and may help gain awareness about the risks. However, we do not have enough information to conclude that standard users read or digital investigators understand these permissions and their implications. Digital investigators need to be on the alert for the presence of malware when examining Android devices, and can benefit from supporting tools that help them understand the capabilities of such malicious code. This paper presents a permission-based Android malware detection system, APK Auditor that uses static analysis to characterize and classify Android applications as benign or malicious. APK Auditor consists of three components: (1) A signature database to store extracted information about applications and analysis results, (2) an Android client which is used by end-users to grant application analysis requests, and (3) a central server responsible for communicating with both signature database and smartphone client and managing whole analysis process. To test system performance, 8762 applications in total, 1853 benign applications from Google's Play Store and 6909 malicious applications from different sources were collected and analyzed by the system developed. The results show that APK Auditor is able to detect most well-known malwares and highlights the ones with a potential in approximately 88% accuracy with a 0.925 specificity.