Android users are constantly threatened by an increasing number of malicious applications (apps), generically called malware. Malware constitutes a serious threat to user privacy, money, device and file integrity. In this paper we note that, by studying their actions, we can classify malware into a small number of behavioral classes, each of which performs a limited set of misbehaviors that characterize them. These misbehaviors can be defined by monitoring features belonging to different Android levels. In this paper we present MADAM, a novel host-based malware detection system for Android devices which simultaneously analyzes and correlates features at four levels: kernel, application, user and package, to detect and stop malicious behaviors. MADAM has been designed to take into account those behaviors characteristics of almost every real malware which can be found in the wild. MADAM detects and effectively blocks more than 96% of malicious apps, which come from three large datasets with about 2,800 apps, by exploiting the cooperation of two parallel classifiers and a behavioral signature-based detector. Extensive experiments, which also includes the analysis of a testbed of 9,804 genuine apps, have been conducted to show the low false alarm rate, the negligible performance overhead and limited battery consumption

No abstract

No abstract

Android applications (apps) pose many risks to their users, e.g., by including code that may threaten user privacy or system integrity. Most of the current security countermeasures for detecting dangerous apps show some weaknesses, mainly related to users' understanding and acceptance. Hence, users would benefit from an effective but simple technique that indicates whether an app is safe or risky to be installed. In this paper, we present MAETROID (Multi-criteria App Evaluator of TRust for AndrOID), a framework to evaluate the trustworthiness of Android apps, i.e., the amount of risk they pose to users, e.g., in terms of confidentiality and integrity. MAETROID performs a multi-criteria analysis of an app at deploy-time and returns a single easyto-understand evaluation of the app's risk level (i.e., Trusted, Medium Risk, and High Risk), aimed at driving the user decision on whether or not installing a new app. The criteria include the set of requested permissions and a set of metadata retrieved from the marketplace, denoting the app quality and popularity. We have tested MAETROID on a set of 11,000 apps both coming from Google Play and from a database of known malicious apps. The results show a good accuracy in both identifying the malicious apps and in terms of false positive rate.