# Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs

arXiv: https://arxiv.org/abs/1808.04706 · HTML: https://mlsanity.com/paper/1808.04706/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2018.

## Built on by (most cited first, 25 of 25)

- [A Survey of Machine Learning for Computer Architecture and Systems](https://mlsanity.com/paper/2102.07952/) (2021)
- [PalmTree: Learning an Assembly Language Model for Instruction Embedding](https://mlsanity.com/paper/2103.03809/) (2021)
- [MalTool: Malicious Tool Attacks on LLM Agents](https://mlsanity.com/paper/2602.12194/) (2026)
- [BinBert: Binary Code Understanding with a Fine-tunable and Execution-aware Transformer](https://mlsanity.com/paper/2208.06692/) (2022)
- [Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts](https://mlsanity.com/paper/2101.02377/) (2021)
- [Semantic-aware Binary Code Representation with BERT](https://mlsanity.com/paper/2106.05478/) (2021)
- [Multi-View Graph Representation for Programming Language Processing: An Investigation into Algorithm Detection](https://mlsanity.com/paper/2202.12481/) (2022)
- [Pre-Training Representations of Binary Code Using Contrastive Learning](https://mlsanity.com/paper/2210.05102/) (2022)
- [EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysis](https://mlsanity.com/paper/2310.01835/) (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)
- [A Survey of Deep Learning Models for Structural Code Understanding](https://mlsanity.com/paper/2205.01293/) (2022)
- [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)
- [Dynamic Neural Control Flow Execution: An Agent-Based Deep Equilibrium Approach for Binary Vulnerability Detection](https://mlsanity.com/paper/2404.08562/) (2024)
- [VEXIR2Vec: An Architecture-Neutral Embedding Framework for Binary Similarity](https://mlsanity.com/paper/2312.00507/) (2023)
- [A Deep Dive into Function Inlining and its Security Implications for ML-based Binary Analysis](https://mlsanity.com/paper/2512.14045/) (2025)
- [Fool Me If You Can: On the Robustness of Binary Code Similarity Detection Models against Semantics-preserving Transformations](https://mlsanity.com/paper/2602.12681/) (2026)
- [PredCoin: Defense against Query-based Hard-label Attack](https://mlsanity.com/paper/2102.02923/) (2021)
- [Pluvio: Assembly Clone Search for Out-of-domain Architectures and Libraries through Transfer Learning and Conditional Variational Information Bottleneck](https://mlsanity.com/paper/2307.10631/) (2023)
- [Bridging the PLC Binary Analysis Gap: A Cross-Compiler Dataset and Neural Framework for Industrial Control Systems](https://mlsanity.com/paper/2502.19725/) (2025)
- [Esim: EVM Bytecode Similarity Detection Based on Stable-Semantic Graph](https://mlsanity.com/paper/2511.12971/) (2025)
- [Attacking the First-Principle: A Black-Box, Query-Free Targeted Mimicry Attack on Binary Function Classifiers](https://mlsanity.com/paper/2605.18231/) (2026)
- [SoK: AI-Augmented Binary Reversing](https://mlsanity.com/paper/2606.17398/) (2026)

## Abstract (arXiv)

Binary code analysis allows analyzing binary code without having access to the corresponding source code. A binary, after disassembly, is expressed in an assembly language. This inspires us to approach binary analysis by leveraging ideas and techniques from Natural Language Processing (NLP), a rich area focused on processing text of various natural languages. We notice that binary code analysis and NLP share a lot of analogical topics, such as semantics extraction, summarization, and classification. This work utilizes these ideas to address two important code similarity comparison problems. (I) Given a pair of basic blocks for different instruction set architectures (ISAs), determining whether their semantics is similar or not; and (II) given a piece of code of interest, determining if it is contained in another piece of assembly code for a different ISA. The solutions to these two problems have many applications, such as cross-architecture vulnerability discovery and code plagiarism detection. We implement a prototype system INNEREYE and perform a comprehensive evaluation. A comparison between our approach and existing approaches to Problem I shows that our system outperforms them in terms of accuracy, efficiency and scalability. And the case studies utilizing the system demonstrate that our solution to Problem II is effective. Moreover, this research showcases how to apply ideas and techniques from NLP to large-scale binary code analysis.