Ashwin Sudhir

2papers

2 Papers

17.8CRMar 18
Pushan: Trace-Free Deobfuscation of Virtualization-Obfuscated Binaries

Ashwin Sudhir, Zion Leonahenahe Basque, Wil Gibbs et al.

In the ever-evolving battle against malware, binary obfuscation techniques are a formidable barrier to effective analysis by both human security analysts and automated systems. In particular, virtualization or VM-based obfuscation is one of the strongest protection mechanisms that evade automated analysis. Despite widespread use of virtualization, existing automated deobfuscation techniques suffer from three major drawbacks. First, they only work on execution traces, which prevents them from recovering all logic in an obfuscated binary. Second, they depend on dynamic symbolic execution, which is expensive and does not scale in practice. Third, they cannot generate "well-formed" code, which prevents existing binary decompilers from generating human-friendly output. This paper introduces PUSHAN, a novel and generic technique for deobfuscating virtualization-obfuscated binaries while overcoming the limitations of existing techniques. PUSHAN is trace-free and avoids path-constraint accumulation by using VPC-sensitive, constraint-free symbolic emulation to recover a complete CFG of the virtualized function. It is the first approach that also decompiles the protected code into high-quality C pseudocode to enable effective analysis. Crucially, PUSHAN circumvents reliance on path satisfiability, a known NP-hard problem that hampers scalability. We evaluate PUSHAN on more than 1,000 binaries, including targets protected by academic state of the art (Tigress) and commercial-strength obfuscators VMProtect and Themida. PUSHAN successfully deobfuscates these binaries, retrieves their complete CFGs, and decompiles them to C pseudocode. We further demonstrate applicability by analyzing a previously unanalyzed VMProtect-obfuscated malware sample from VirusTotal, where our decompiled output enables LLM-assisted code simplification, reuse, and program understanding.

HCFeb 8, 2020
Lessons Learned Developing and Extending a Visual Analytics Solution for Investigative Analysis of Scamming Activities

Ronak Tanna, Shivam Dhar, Ashwin Sudhir et al.

Cybersecurity analysts work on large communication data sets to perform investigative analysis by painstakingly going over thousands of email conversations to find potential scamming activities and the network of cyber scammers. Traditionally,experts used email clients, database systems and text editors to perform this investigation. With the advent of technology,elaborate tools that summarize data more efficiently by using cutting edge data visualization techniques have come out. Beagle[1] is one such tool which visualizes the large communication data using different panels such that the inspector has better chances of finding the scam network. This paper is a report on our work to implement and improve the work done by Jay Koven et al. [1]. We have proposed and demonstrated via implementation, a few more visualizations that we feel would help in grouping and analyzing the e-mail data more efficiently. Lastly, we have also presented a case study that shows the potential use of our tool in a real-world scenario.