Usage of the execution stack at run-time captures the dynamic state of programs and can be used to derive useful insights into the program behaviour. The stack usage information can be used to identify and debug performance and security aspects of applications. Binary run-time instrumentation techniques are well known to capture the memory access traces during program execution. Tracing the program in entirety and filtering out stack specific accesses is a commonly used technique for stack related analysis. However, applying vanilla tracing techniques (using tools like Intel Pin) for multi-threaded programs has challenges such as identifying the stack areas to perform efficient run-time tracing.

With the large-scale adaptation of Android OS and ever-increasing contributions in the Android application space, Android has become the number one target of malware writers. In recent years, a large number of automatic malware detection and classification systems have evolved to tackle the dynamic nature of malware growth using either static or dynamic analysis techniques. Performance of static malware detection methods degrade due to the obfuscation attacks. Although many benchmark datasets are available to measure the performance of malware detection and classification systems, only a single obfuscated malware dataset (PRAGuard) is available to showcase the efficacy of the existing malware detection systems against the obfuscation attacks. PRAGuard contains outdated samples till March 2013 and does not represent the latest application categories. Moreover, PRAGuard does not provide the family information for malware because of which PRAGuard can not be used to evaluate the efficacy of the malware family classification systems.