N2NMatcher: Towards Inlining-Resilient Binary Decomposition and Module Matching

• URL: http://arxiv.org/abs/2608.10043v1
• Date: Mon, 10 Aug 2026 09:37:42 GMT
• Title: N2NMatcher: Towards Inlining-Resilient Binary Decomposition and Module Matching
• Abstract summary: Program-level Binary Code Similarity Analysis (BCSA) aims to identify semantically similar code regions across binary programs.<n>N2NMatcher is an inlining-resilient framework for binary decomposition and module matching.
• Score: 10.071819176260677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Program-level Binary Code Similarity Analysis (BCSA) aims to identify semantically similar code regions across binary programs, serving as a fundamental technique for software plagiarism detection, vulnerability search, and malware analysis. Existing approaches often decompose binaries into modules following the structure of function call graphs (FCGs) and then match these modules by their contained functions. However, function inlining changes both FCG structures and binary function semantics, making existing decomposition and module matching methods less effective. In this work, we propose N2NMatcher, an inlining-resilient framework for binary decomposition and module matching. We first conduct an empirical study to examine whether binaries still contain functions that provide stable module boundaries across compilation settings. N2NMatcher learns to predict such functions as anchors using a hierarchical graph neural network that encodes binary ACFG-FCG representations built from opcode sequences, control-flow structures, and FCG calling context. It then performs anchor-bounded decomposition and matches the generated modules using learned module graph embeddings. Experimental results show that N2NMatcher improves both the decomposition quality and module matching accuracy compared to existing works, enabling more effective program-level BCSA.

Related papers

• ReSIM: Re-ranking Binary Similarity Embeddings to Improve Function Search Performance [6.94939106765873]
  We introduce ReSIM, a novel and enhanced function search system that complements embedding-based search with a neural re-ranker.<n>We evaluate ReSIM across seven embedding models on two benchmark datasets, demonstrating consistent improvements in search effectiveness.
  arXiv  Detail & Related papers  (2026-02-10T08:57:49Z)
• Cross-modal Retrieval Models for Stripped Binary Analysis [62.89251403093734]
  BinSeek is the first two-stage cross-modal retrieval framework for stripped binary code analysis.<n>It consists of two models: BinSeekEmbedding is trained on large-scale dataset to learn the semantic relevance of the binary code.<n>BinSeek-Reranker learns to carefully judge the relevance of the candidate code to the description with context augmentation.
  arXiv  Detail & Related papers  (2025-12-11T07:58:10Z)
• What Makes the Best Decomposition? Investigating Binary Decomposition Under FCG Variance [11.950334662727848]
  We conduct the first systematic empirical study on the variance of function call graphs compiled by various compilation settings.<n>We find that the size of FCGs changes dramatically, while the FCGs are still linked by three different kinds of mappings.<n>We propose a method to identify the optimal decomposition and compare the existing decomposition works with the optimal decomposition.
  arXiv  Detail & Related papers  (2025-06-24T08:43:28Z)
• Functional Matching of Logic Subgraphs: Beyond Structural Isomorphism [13.064477057274226]
  Subgraph matching is foundational for numerous Electronic Design Automation (EDA) applications.<n>We introduce the concept of functional subgraph matching, a novel approach that identifies whether a given logic function is implicitly present within a larger circuit.
  arXiv  Detail & Related papers  (2025-05-28T05:31:49Z)
• Bi-Directional Transformers vs. word2vec: Discovering Vulnerabilities in Lifted Compiled Code [4.956066467858057]
  This research explores vulnerability detection using natural language processing (NLP) embedding techniques with word2vec, BERT, and RoBERTa. Long short-term memory (LSTM) neural networks were trained on embeddings from encoders created using approximately 48k LLVM functions from the Juliet dataset.
  arXiv  Detail & Related papers  (2024-05-31T03:57:19Z)
• FoC: Figure out the Cryptographic Functions in Stripped Binaries with LLMs [51.898805184427545]
  We propose a novel framework called FoC to Figure out the Cryptographic functions in stripped binaries.<n>We first build a binary large language model (FoC-BinLLM) to summarize the semantics of cryptographic functions in natural language.<n>We then build a binary code similarity model (FoC-Sim) upon the FoC-BinLLM to create change-sensitive representations and use it to retrieve similar implementations of unknown cryptographic functions in a database.
  arXiv  Detail & Related papers  (2024-03-27T09:45:33Z)
• Compacting Binary Neural Networks by Sparse Kernel Selection [58.84313343190488]
  This paper is motivated by a previously revealed phenomenon that the binary kernels in successful BNNs are nearly power-law distributed. We develop the Permutation Straight-Through Estimator (PSTE) that is able to not only optimize the selection process end-to-end but also maintain the non-repetitive occupancy of selected codewords. Experiments verify that our method reduces both the model size and bit-wise computational costs, and achieves accuracy improvements compared with state-of-the-art BNNs under comparable budgets.
  arXiv  Detail & Related papers  (2023-03-25T13:53:02Z)
• UniASM: Binary Code Similarity Detection without Fine-tuning [2.2329530239800035]
  We propose a novel rich-semantic function representation technique to ensure the model captures the intricate nuances of binary code.<n>We introduce the first UniLM-based binary code embedding model, named UniASM, which includes two newly designed training tasks.<n>The experimental results show that UniASM outperforms the state-of-the-art (SOTA) approaches on the evaluation datasets.
  arXiv  Detail & Related papers  (2022-10-28T14:04:57Z)
• Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies [52.691032025163175]
  Existing Binary Neural Networks (BNNs) operate mainly on local convolutions with binarization function. We present new designs of binary neural modules, which enables leading binary neural modules by a large margin.
  arXiv  Detail & Related papers  (2022-09-03T11:51:04Z)
• Training Free Graph Neural Networks for Graph Matching [103.45755859119035]
  TFGM is a framework to boost the performance of Graph Neural Networks (GNNs) based graph matching without training. Applying TFGM on various GNNs shows promising improvements over baselines.
  arXiv  Detail & Related papers  (2022-01-14T09:04:46Z)
• Evaluating Modules in Graph Contrastive Learning [29.03038320344791]
  We propose a framework that decomposes graph contrastive learning models into four modules. We conduct experiments on node and graph classification tasks. We release our implementations and results as OpenGCL, a modularized toolkit.
  arXiv  Detail & Related papers  (2021-06-15T14:14:23Z)
• BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism [106.43103176833371]
  Collaborative Filtering (CF) based recommendation methods have been widely studied. We propose a novel recommendation model named Balanced Collaborative Filtering Network (BCFNet) In addition, an attention mechanism is designed to better capture the hidden information within implicit feedback and strengthen the learning ability of the neural network.
  arXiv  Detail & Related papers  (2021-03-10T14:59:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.