Steffen Enders, Eva-Maria C. Behner, Niklas Bergmann, Mariia Rybalka, Elmar Padilla (Fraunhofer FKIE, Germany), Er Xue Hui, Henry Low, Nicholas Sim (DSO National Laboratories, Singapore)

Analyzing third-party software such as malware is a crucial task for security analysts. Although various approaches for automatic analysis exist and are the subject of ongoing research, analysts often have to resort to manual static analysis to get a deep understanding of a given binary sample. Since the source code of provided samples is rarely available, analysts regularly employ decompilers for easier and faster comprehension than analyzing a binary’s disassembly.

In this paper, we introduce our decompilation approach dewolf. We describe a variety of improvements over the previous academic state-of-the-art decompiler and some novel algorithms to enhance readability and comprehension, focusing on manual analysis. To evaluate our approach and to obtain a better insight into the analysts’ needs, we conducted three user surveys. The results indicate that dewolf is suitable for malware comprehension and that its output quality noticeably exceeds Ghidra and Hex-Rays in certain aspects. Furthermore, our results imply that decompilers aiming at manual analysis should be highly configurable to respect individual user preferences. Additionally, future decompilers should not necessarily follow the unwritten rule to stick to the code-structure dictated by the assembly in order to produce readable output. In fact, the few cases where dewolf already cracks this rule leads to its results considerably exceeding other decompilers. We publish a prototype implementation of dewolf and all survey results [1], [2].

View More Papers

An Exploratory study of Malicious Link Posting on Social...

Muhammad Hassan, Mahnoor Jameel, Masooda Bashir (University of Illinois at Urbana Champaign)

Read More

Preventing SIM Box Fraud Using Device Model Fingerprinting

BeomSeok Oh (KAIST), Junho Ahn (KAIST), Sangwook Bae (KAIST), Mincheol Son (KAIST), Yonghwa Lee (KAIST), Min Suk Kang (KAIST), Yongdae Kim (KAIST)

Read More

CHKPLUG: Checking GDPR Compliance of WordPress Plugins via Cross-language...

Faysal Hossain Shezan (University of Virginia), Zihao Su (University of Virginia), Mingqing Kang (Johns Hopkins University), Nicholas Phair (University of Virginia), Patrick William Thomas (University of Virginia), Michelangelo van Dam (in2it), Yinzhi Cao (Johns Hopkins University), Yuan Tian (UCLA)

Read More