Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild

In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild. In particular, we focus on four popular obfuscation approaches: identifier renaming, string encryption, Java reflection, and packing. To obtain the meaningful statistical results, we designed efficient and lightweight detection models for each obfuscation technique and applied them to our massive APK datasets (collected from Google Play, multiple third-party markets, and malware databases). We have learned several interesting facts from the result. For example, malware authors use string encryption more frequently, and more apps on third-party markets than Google Play are packed. We are also interested in the explanation of each finding. Therefore we carry out in-depth code analysis on some Android apps after sampling. We believe our study will help developers select the most suitable obfuscation approach, and in the meantime help researchers improve code analysis systems in the right direction

Instructions-Based Detection of Sophisticated Obfuscation and Packing

Every day thousands of malware are released online. The vast majority of these malware employ some kind of obfuscation ranging from simple XOR encryption, to more sophisticated anti-analysis, packing and encryption techniques. Dynamic analysis methods can unpack the file and reveal its hidden code. However, these methods are very time consuming when compared to static analysis. Moreover, considering the large amount of new malware being produced daily, it is not practical to solely depend on dynamic analysis methods. Therefore, finding an effective way to filter the samples and delegate only obfuscated and suspicious ones to more rigorous tests would significantly improve the overall scanning process. Current techniques of identifying obfuscation rely mainly on signatures of known packers, file entropy score, or anomalies in file header. However, these features are not only easily bypass-able, but also do not cover all types of obfuscation. In this paper, we introduce a novel approach to identify obfuscated files based on anomalies in their instructions-based characteristics. We detect the presence of interleaving instructions which are the result of the opaque predicate anti-disassembly trick, and present distinguishing statistical properties based on the opcodes and control flow graphs of obfuscated files. Our detection system combines these features with other file structural features and leads to a very good result of detecting obfuscated malware

Acceleration of Statistical Detection of Zero-day Malware in the Memory Dump Using CUDA-enabled GPU Hardware

This paper focuses on the anticipatory enhancement of methods of detecting stealth software. Cyber security detection tools are insufficiently powerful to reveal the most recent cyber-attacks which use malware. In this paper, we will present first an idea of the highest stealth malware, as this is the most complicated scenario for detection because it combines both existing anti-forensic techniques together with their potential improvements. Second, we will present new detection methods which are resilient to this hidden prototype. To help solve this detection challenge, we have analyzed Windows’ memory content using a new method of Shannon Entropy calculation; methods of digital photogrammetry; the Zipf–Mandelbrot law, as well as by disassembling the memory content and analyzing the output. Finally, we present an idea and architecture of the software tool, which uses CUDA-enabled GPU hardware, to speed-up memory forensics. All three ideas are currently a work in progress. Keywords: rootkit detection, anti-forensics, memory analysis, scattered fragments, anticipatory enhancement, CUDA

Acceleration of Statistical Detection of Zero-day Malware in the Memory Dump Using CUDA-enabled GPU Hardware

This paper focuses on the anticipatory enhancement of methods of detecting stealth software. Cyber security detection tools are insufficiently powerful to reveal the most recent cyber-attacks which use malware. In this paper, we will present first an idea of the highest stealth malware, as this is the most complicated scenario for detection because it combines both existing anti-forensic techniques together with their potential improvements. Second, we present new detection methods, which are resilient to this hidden prototype. To help solve this detection challenge, we have analyzed Windows memory content using a new method of Shannon Entropy calculation; methods of digital photogrammetry; the Zipf Mandelbrot law, as well as by disassembling the memory content and analyzing the output. Finally, we present an idea and architecture of the software tool, which uses CUDA enabled GPU hardware to speed-up memory forensics. All three ideas are currently a work in progress