Detection of repackaged mobile applications through a collaborative approach

none4noRepackaged applications are based on genuine applications, but they subtlety include some modifications. In particular, trojanized applications are one of the most dangerous threats for smartphones. Malware code may be hidden inside applications to access private data or to leak user credit. In this paper, we propose a contract-based approach to detect such repackaged applications, where a contract specifies the set of legal actions that can be performed by an application. Current methods to generate contracts lack information from real usage scenarios, thus being inaccurate and too coarse-grained. This may result either in generating too many false positives or in missing misbehaviors when verifying the compliance between the application and the contract. In the proposed framework, application contracts are generated dynamically by a central server merging execution traces collected and shared continuously by collaborative users executing the application. More precisely, quantitative information extracted from execution traces is used to define a contract describing the expected application behavior, which is deployed to the cooperating users. Then, every user can use the received contract to check whether the related application is either genuine or repackaged. Such a verification is based on an enforcement mechanism that monitors the application execution at run-time and compares it against the contract through statistical tests.openAlessandro Aldini; Fabio Martinelli; Andrea Saracino; Daniele SgandurraAldini, Alessandro; Fabio, Martinelli; Andrea, Saracino; Daniele, Sgandurr

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

Leveraging the Use of API Call Traces for Mobile Security

The growing popularity of Android applications has generated increased concerns over the danger of piracy and the spread of malware. A popular way to distribute malware in the mobile world is through the repackaging of legitimate apps. This process consists of downloading, unpacking, manipulating, recompiling an application, and publishing it again in an app store. In this thesis, we conduct an empirical study of over 15,000 apps to gain insights into the factors that drive the spread of repackaged apps. We also examine the motivations of developers who publish repackaged apps and those of users who download them, as well as the factors that determine which apps are chosen for repackaging, and the ways in which the apps are modified during the repackaging process. We have also studied android applications structure to investigate the locations where malicious code are more probable to be embedded into legitimate applications. We observed that service components contain key characteristics that entice attackers to misuse them. Therefore, we have focus on studying the behavior of malicious and benign services. Whereas benign services tend to inform the user of the background operations, malicious services tend to do long running operations and have a loose connection with rest of the code. These findings lead us to propose an approach to detect malware by studying the services’ behavior. To model the services’ behavior, we used API calls as feature sets. We proposed a hybrid approach using static and dynamic analysis to extract the API calls through the service lifecycle. Finally, we used the list of API calls preponderantly present in both malware as well as benign services as the feature set. We applied machine learning algorithms to use the feature set to classify malicious services and benign services

Detection of repackaged mobile applications through a collaborative approach

Repackaged applications are based on genuine applications, but they subtlety include some modifications. In particular, trojanized applications are one of the most dangerous threats for smartphones. Malware code may be hidden inside applications to access private data or to leak user credit. In this paper, we propose a contract-based approach to detect such repackaged applications, where a contract specifies the set of legal actions that can be performed by an application. Current methods to generate contracts lack information from real usage scenarios, thus being inaccurate and too coarse-grained. This may result either in generating too many false positives or in missing misbehaviors when verifying the compliance between the application and the contract. In the proposed framework, application contracts are generated dynamically by a central server merging execution traces collected and shared continuously by collaborative users executing the application. More precisely, quantitative information extracted from execution traces is used to define a contract describing the expected application behavior, which is deployed to the cooperating users. Then, every user can use the received contract to check whether the related application is either genuine or repackaged. Such a verification is based on an enforcement mechanism that monitors the application execution at run-time and compares it against the contract through statistical tests