Jonathan Crussell (Sandia National Laboratories)

Malware analysis relies on evolving tools that undergo continuous improvement and refinement. One such tool is Ghidra, released as open-source in 2019, which has seen 39 public releases and 13,000 commits as of October 2024. In this paper, we examine the impact of these updates on code similarity analysis for the same set of input files. Additionally, we measure how the underlying version of Ghidra affects simple metrics such as analysis time, error counts, and the number of functions identified. Our case studies reveal that Ghidra’s effectiveness varies depending on the specific file analyzed, highlighting the importance of context in evaluating tool performance.
We do not yet have an answer to the question posed in the title of this paper. In general, Ghidra has certainly improved in the years since it was released. Developers have fixed countless bugs, added substantial new features, and supported several new program formats. However, we observe that better is highly nuanced. We encourage the community to approach version upgrades with caution, as the latest release may not always provide superior results for every use case. By fostering a nuanced understanding of Ghidra’s advancements, we aim to contribute to more informed decision-making regarding tool adoption and usage in malware analysis and other binary analysis domains.

View More Papers

Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language...

Aydin Abadi (Newcastle University), Vishnu Asutosh Dasu (Pennsylvania State University), Sumanta Sarkar (University of Warwick)

Read More

Rondo: Scalable and Reconfiguration-Friendly Randomness Beacon

Xuanji Meng (Tsinghua University), Xiao Sui (Shandong University), Zhaoxin Yang (Tsinghua University), Kang Rong (Blockchain Platform Division,Ant Group), Wenbo Xu (Blockchain Platform Division,Ant Group), Shenglong Chen (Blockchain Platform Division,Ant Group), Ying Yan (Blockchain Platform Division,Ant Group), Sisi Duan (Tsinghua University)

Read More

PISE: Protocol Inference using Symbolic Execution and Automata Learning

Ron Marcovich, Orna Grumberg, Gabi Nakibly (Technion, Israel Institute of Technology)

Read More