# Revisiting Binary Code Similarity Analysis using Interpretable Feature Engineering and Lessons Learned

arXiv: https://arxiv.org/abs/2011.10749 · HTML: https://mlsanity.com/paper/2011.10749/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2020.

## Built on by (most cited first, 14 of 14)

- [Assemblage: Automatic Binary Dataset Construction for Machine Learning](https://mlsanity.com/paper/2405.03991/) (2024)
- [kTrans: Knowledge-Aware Transformer for Binary Code Embedding](https://mlsanity.com/paper/2308.12659/) (2023)
- [XFL: Naming Functions in Binaries with Extreme Multi-label Learning](https://mlsanity.com/paper/2107.13404/) (2021)
- [Callee: Recovering Call Graphs for Binaries with Transfer and Contrastive Learning](https://mlsanity.com/paper/2111.01415/) (2021)
- [FASER: Binary Code Similarity Search through the use of Intermediate Representations](https://mlsanity.com/paper/2310.03605/) (2023)
- [FuncFooler: A Practical Black-box Attack Against Learning-based Binary Code Similarity Detection Methods](https://mlsanity.com/paper/2208.14191/) (2022)
- [UniASM: Binary Code Similarity Detection without Fine-tuning](https://mlsanity.com/paper/2211.01144/) (2022)
- [On the Lack of Robustness of Binary Function Similarity Systems](https://mlsanity.com/paper/2412.04163/) (2024)
- [The Inconvenient Truths of Ground Truth for Binary Analysis](https://mlsanity.com/paper/2210.15079/) (2022)
- [A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer](https://mlsanity.com/paper/2412.11177/) (2024)
- [A Deep Dive into Function Inlining and its Security Implications for ML-based Binary Analysis](https://mlsanity.com/paper/2512.14045/) (2025)
- [Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call Graphlets](https://mlsanity.com/paper/2406.02606/) (2024)
- [ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage](https://mlsanity.com/paper/2605.21615/) (2026)
- [SoK: AI-Augmented Binary Reversing](https://mlsanity.com/paper/2606.17398/) (2026)

## Abstract (arXiv)

Binary code similarity analysis (BCSA) is widely used for diverse security applications, including plagiarism detection, software license violation detection, and vulnerability discovery. Despite the surging research interest in BCSA, it is significantly challenging to perform new research in this field for several reasons. First, most existing approaches focus only on the end results, namely, increasing the success rate of BCSA, by adopting uninterpretable machine learning. Moreover, they utilize their own benchmark, sharing neither the source code nor the entire dataset. Finally, researchers often use different terminologies or even use the same technique without citing the previous literature properly, which makes it difficult to reproduce or extend previous work. To address these problems, we take a step back from the mainstream and contemplate fundamental research questions for BCSA. Why does a certain technique or a certain feature show better results than the others? Specifically, we conduct the first systematic study on the basic features used in BCSA by leveraging interpretable feature engineering on a large-scale benchmark. Our study reveals various useful insights on BCSA. For example, we show that a simple interpretable model with a few basic features can achieve a comparable result to that of recent deep learning-based approaches. Furthermore, we show that the way we compile binaries or the correctness of underlying binary analysis tools can significantly affect the performance of BCSA. Lastly, we make all our source code and benchmark public and suggest future directions in this field to help further research.