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Android has become the most widely used mobile operating system (OS) in recent years. There is much research on methods for detecting malicious Android applications. Dynamic analysis methods detect such applications by evaluating their behaviour during execution. However, such mechanisms may be ineffective as malware is often able to disable antimalware software. This paper presents the design of...
The high usability of smartphones and tablets is embraced by consumers as well as the private and public sector. However, especially in the non-consumer area the factor security plays a decisive role for the platform selection process. All of the current companies within the mobile device sector added a wide range of security features to the initially consumer-oriented devices (Apple, Google, Microsoft),...
In rural Africa, where land-based Internet connectivity is a huge problem due to a lack of infrastructure, wireless Internet connectivity and the adoption of mobile devices are a huge success. However, when it comes to mobile payments, Africa still has a long way to go. The biggest concerns with mobile payment applications are a lack of readily available banks, vendors and other shops that can accept...
Modern mobile devices are able to run a wide range of thirdparty/service provider applications that provide users with a variety of attractive services. These applications have their own databases on the mobile device; user data pertaining to the application are stored there. The popularity comes with a price: attackers are interested in exploiting the weaknesses of the mobile systems to manipulate...
In this paper we apply Machine Learning (ML) techniques on static features that are extracted from Android's application files for the classification of the files. Features are extracted from Android's Java byte-code (i.e.,.dex files) and other file types such as XML-files. Our evaluation focused on classifying two types of Android applications: tools and games. Successful differentiation between...
This paper presents a distributed Support Vector Machine (SVM) algorithm in order to detect malicious software (malware) on a network of mobile devices. The light-weight system monitors mobile user activity in a distributed and privacy-preserving way using a statistical classification model which is evolved by training with examples of both normal usage patterns and unusual behavior. The system is...
We present various approaches for mitigating malware on mobile devices which we have implemented and evaluated on Google Android. Our work is divided into the following three segments: a host-based intrusion detection framework; an implementation of SELinux in Android; and static analysis of Android application files.
Application markets have rapidly become a widely popular mechanism for expanding the features and utility of mobile devices such as cell phones. The cottage industries that sprung up around these markets serve millions of Patrick McDaniel and William Enck Pennsylvania State University applications daily to a ready user audience. Markets entice developers by placing low economic and technical barriers...
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