AI Summary & Key Takeaways
AI InsightThisapproach was applied on existing datasets; the results were compared and accuracy level was determined.For Android operatingsystem, it is aimed to determine the malicious applications via this newly developed method and to create a safer Android atmospherefor users.
📌 3 Key Takeaways
Abstract
Mobile applications create their own security and privacy models through permission based models. Applications, if they requireto access any sensitive data in mobile devices that they are downloaded on, in order to do the needed system call for this access,they have to define only required permissions. However, some applications may request extra permissions which they do not needand may use these permissions for suspicious database access they do later. In this study, the aim is to determine those extrarequested permissions and to use this on the security and privacy model. According to the study, through the determined methodology,risk values of applications are determined in the light of pre-determined levels within datasets. It is an approach that usesstatic analysis and code analysis together. According to this approach, the permissions that the applications request and use aredetermined separately and the applications that request extra permissions are discovered. Then, via the produced formula, suspicionvalue of every application is determined and applications are classified as malicious or benignant according to this value. Thisapproach was applied on existing datasets; the results were compared and accuracy level was determined.For Android operatingsystem, it is aimed to determine the malicious applications via this newly developed method and to create a safer Android atmospherefor users.
Özet
Mobil uygulamalar izin tabanlı modelleri sayesinde kendi güvenlik ve gizlilik modellerini oluştururlar. Uygulamalar, yüklendikleri