AdSplit: Separating smartphone advertising from applications

A wide variety of smartphone applications today rely on third-party advertising services, which provide libraries that are linked into the hosting application. This situation is undesirable for both the application author and the advertiser. Advertising libraries require additional permissions, resulting in additional permission requests to users. Likewise, a malicious application could simulate the behavior of the advertising library, forging the user's interaction and effectively stealing money from the advertiser. This paper describes AdSplit, where we extended Android to allow an application and its advertising to run as separate processes, under separate user-ids, eliminating the need for applications to request permissions on behalf of their advertising libraries. We also leverage mechanisms from Quire to allow the remote server to validate the authenticity of client-side behavior. In this paper, we quantify the degree of permission bloat caused by advertising, with a study of thousands of downloaded apps. AdSplit automatically recompiles apps to extract their ad services, and we measure minimal runtime overhead. We also observe that most ad libraries just embed an HTML widget within and describe how AdSplit can be designed with this in mind to avoid any need for ads to have native code

Malware detection techniques for mobile devices

Mobile devices have become very popular nowadays, due to its portability and high performance, a mobile device became a must device for persons using information and communication technologies. In addition to hardware rapid evolution, mobile applications are also increasing in their complexity and performance to cover most needs of their users. Both software and hardware design focused on increasing performance and the working hours of a mobile device. Different mobile operating systems are being used today with different platforms and different market shares. Like all information systems, mobile systems are prone to malware attacks. Due to the personality feature of mobile devices, malware detection is very important and is a must tool in each device to protect private data and mitigate attacks. In this paper, analysis of different malware detection techniques used for mobile operating systems is provides. The focus of the analysis will be on the to two competing mobile operating systems - Android and iOS. Finally, an assessment of each technique and a summary of its advantages and disadvantages is provided. The aim of the work is to establish a basis for developing a mobile malware detection tool based on user profiling.Comment: 11 pages, 6 figure

Enforcing Application Security on Android Mobile Devices

Security in new generation mobile devices is currently a problem of capital importance. Smartphones and tablets have become extremely popular in the last years, especially in developed country where smartphones and tablets account for 95% of active mobile devices. Due to their popularity, these devices have fast drawn the attention of malicious developers. Attackers have started to implement and distribute applications able to harm user’s privacy, user’s money and even device and data integrity. Malicious developers have cleverly exploited the simplicity of app distribution, the sensitivity of information and operation accessible through mobile devices, together with the user limited attention to security issues. This thesis presents the study, design and implementation of a multi-component security framework for the popular Android operative system. The aim of this thesis is to provide a lightweight and user friendly security tool, extensible and modular, able to tackle current and future security threats on Android devices. The framework exploits white list-based methodologies to detect at runtime malicious behaviors of application, without being prone to the problem of zero-day-attacks (i.e. new threats not yet discovered by the community). The white-list approach is combined with a black-list security enforcement, to reduce the likelihood of false alarms and to tackle known misbehaviors before they effectively take place. Moreover the framework also combines static and dynamic analysis. It exploits probabilistic contract theory and app metadata to detect dangerous applications before they are installed (static analysis). Furthermore, detects and stop malicious kernel level events and API calls issued by applications at runtime (dynamic analysis), to avoid harm to user and her device. The framework is configurable and can be both totally transparent to the user, or have a stronger interaction when the user is more interested in a security awareness of her device. The presented security framework has been extensively tested against a testbed of more than 12000 applications including two large Android malware databases. Detection rate (95%) and false positive rate (1 per day) prove the effectiveness of the presented framework. Furthermore, a study of usability which includes energy evaluation and more than 200 user feedback is presented. These results show both the limited overhead (4% battery, 1.4% performance) imposed by the framework and the good user acceptance

Ghera: A Repository of Android App Vulnerability Benchmarks

Security of mobile apps affects the security of their users. This has fueled the development of techniques to automatically detect vulnerabilities in mobile apps and help developers secure their apps; specifically, in the context of Android platform due to openness and ubiquitousness of the platform. Despite a slew of research efforts in this space, there is no comprehensive repository of up-to-date and lean benchmarks that contain most of the known Android app vulnerabilities and, consequently, can be used to rigorously evaluate both existing and new vulnerability detection techniques and help developers learn about Android app vulnerabilities. In this paper, we describe Ghera, an open source repository of benchmarks that capture 25 known vulnerabilities in Android apps (as pairs of exploited/benign and exploiting/malicious apps). We also present desirable characteristics of vulnerability benchmarks and repositories that we uncovered while creating Ghera.Comment: 10 pages. Accepted at PROMISE'1