Abstract. Together with the massive expansion of smartphones, tablets, and other smart devices, we can notice a growing number of malware threats targeting these platforms. Software security companies are not prepared for such diversity of target platforms and there are only few techniques for platform-independent malware analysis. This is a major security issue these days. In this paper, we propose a concept of a retargetable reverse compiler (i.e. a decompiler), which is in an early stage of development. The retargetable decompiler transforms platformspecific binary applications into a high-level language (HLL) representation, which can be further analyzed in a uniform way. This tool will help with a static platform-independent malware analysis. Our unique solution is based on an exploitation of two systems that were originally not intended for such an application-the architecture description language (ADL) ISAC for a platform description and the LLVM Compiler System as the core of the decompiler. In this study, we show that our tool can produce highly readable HLL code.
The fast and accurate processor simulator is an essential tool for effective design of modern high-performance application-specific instruction set processors. The nowadays trend of ASIP design is focused on automatic simulator generation based on a processor description in an architecture description language. The simulator is used for testing and validation of designed processor or target application. Furthermore, the simulator can produce the profiling information. This information can aid design space exploration and the processor and target application optimization. In this paper, we present the concept of automatically generated just-intime translated simulator with the profiling capabilities. This simulator is very fast, and it is generated in a short time. It can be even used for simulation of special applications, such as applications with self-modifying code or applications for systems with external memories. The experimental results can be found at the end of the paper.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.