Not only can the user firewall allow or block intents, but it can even modify them to a controlled extent. The app, called a “user firewall”, can then receive intents as they enter the system and inspect them. This allows the access control logic to be placed inside a normal application and reached via the interface. for performing access control from the point of interception by placing an interface in the Android framework. We propose Intentio Ex Machina (IEM), an access control solution for Android intent security. The increased volume of intent IPC present in Android devices, coupled with intent’s ability to implicitly find valid receivers for IPC, bring about new security challenges. Read moreĪndroid’s intent framework serves as the primary method for interprocess communication (IPC) among apps. The runtime overhead is less than 63%, which is significantly lower than that of existing approaches. We measured the overhead based on a representative benchmark app, and found that both the memory and CPU overhead are less than 10%. Our evaluation shows that Dagger can effectively vet sensitive behaviors in apps, especially for those using complex obfuscation techniques.
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We evaluate Dagger on both a set of over 1200 known malicious Android apps, and a second set of 1000 apps randomly selected from a corpus of over 18,000 Google Play apps. Dagger identifies behaviors by matching the provenance graph with the behavior graph patterns that are previously extracted from the internal working logic of the Android framework. A data provenance graph is then built to record the interactions between the app and the phone system based on these three types of information. App process details are extracted from the Android /proc file system. Binder transactions are recorded by accessing Binder module logs via sysfs. System call collection is performed via Strace, a low-latency utility for Linux and other Unix-like systems. low-level execution information at runtime: system calls, Android Binder transactions, and app process details. More specifically, Dagger uses three types of. Instead, Dagger reconstructs the program semantics by tracking provenance relationships and observing apps’ runtime interactions with the phone platform. Dagger avoids costly instrumentation of virtual machines or modifications to the Android kernel. We propose Dagger, a lightweight system to dynamically vet sensitive behaviors in Android apps. Even though the invoke-dynamic instruction with the dual stack implementation is slowed down by doing a fair amount of computations, the comparison shows that the dual stack implementation greatly improves the execution time. Then we detail how we handle allocations from the combiner interpreter using a stack object (dual stack) allocated in the heap and avoid allocations done by the continuation mechanism.Īt the end, we present a comparison of the execution time with and without the allocation minimization.
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First we explain how we avoid allocations from the C interpreter using a stack frame allocation (fake stack frame).
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This paper describes how we minimize the allocation done by our implementation of the JSR 292 on Android. The implementation presented in is slowed down by several allocations done by the C interpreter and the combiner interpreter.
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Dalvik which uses a C interpreter for direct method handles and a Java flexible interpreter for the "combiner" method handles. Android 292 is our implementation of the JSR 292 on top of. Android, the Google operating system for embedded devices, uses for its applicative part a Java-based virtual machine called Dalvik. To improve and simplify the implementation of dynamically typed languages on top of the Java virtual machine, Java 7 introduced a new instructions set as well as a new API specified by the JSR 292.