BinSimDB: Benchmark Dataset Construction for Fine-Grained Binary Code Similarity Analysis

• URL: http://arxiv.org/abs/2410.10163v1
• Date: Mon, 14 Oct 2024 05:13:48 GMT
• Title: BinSimDB: Benchmark Dataset Construction for Fine-Grained Binary Code Similarity Analysis
• Authors: Fei Zuo, Cody Tompkins, Qiang Zeng, Lannan Luo, Yung Ryn Choe, Junghwan Rhee,
• Abstract summary: We construct a benchmark dataset for fine-grained binary code similarity analysis called BinSimDB. Specifically, we propose BMerge and BPair algorithms to bridge the discrepancies between two binary code snippets. The experimental results demonstrate that BinSimDB significantly improves the performance of binary code similarity comparison.
• Score: 6.093226756571566
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Binary Code Similarity Analysis (BCSA) has a wide spectrum of applications, including plagiarism detection, vulnerability discovery, and malware analysis, thus drawing significant attention from the security community. However, conventional techniques often face challenges in balancing both accuracy and scalability simultaneously. To overcome these existing problems, a surge of deep learning-based work has been recently proposed. Unfortunately, many researchers still find it extremely difficult to conduct relevant studies or extend existing approaches. First, prior work typically relies on proprietary benchmark without making the entire dataset publicly accessible. Consequently, a large-scale, well-labeled dataset for binary code similarity analysis remains precious and scarce. Moreover, previous work has primarily focused on comparing at the function level, rather than exploring other finer granularities. Therefore, we argue that the lack of a fine-grained dataset for BCSA leaves a critical gap in current research. To address these challenges, we construct a benchmark dataset for fine-grained binary code similarity analysis called BinSimDB, which contains equivalent pairs of smaller binary code snippets, such as basic blocks. Specifically, we propose BMerge and BPair algorithms to bridge the discrepancies between two binary code snippets caused by different optimization levels or platforms. Furthermore, we empirically study the properties of our dataset and evaluate its effectiveness for the BCSA research. The experimental results demonstrate that BinSimDB significantly improves the performance of binary code similarity comparison.

Related papers

• Is Function Similarity Over-Engineered? Building a Benchmark [37.33020176141435]
  We build a new benchmark for binary function similarity detection consisting of high-quality datasets and tests that better reflect real-world use cases. Our benchmark reveals that a new, simple basline, one which looks at only the raw bytes of a function, and requires no disassembly or other pre-processing, is able to achieve state-of-the-art performance in multiple settings.
  arXiv  Detail & Related papers  (2024-10-30T03:59:46Z)
• Binary Code Similarity Detection via Graph Contrastive Learning on Intermediate Representations [52.34030226129628]
  Binary Code Similarity Detection (BCSD) plays a crucial role in numerous fields, including vulnerability detection, malware analysis, and code reuse identification. In this paper, we propose IRBinDiff, which mitigates compilation differences by leveraging LLVM-IR with higher-level semantic abstraction. Our extensive experiments, conducted under varied compilation settings, demonstrate that IRBinDiff outperforms other leading BCSD methods in both One-to-one comparison and One-to-many search scenarios.
  arXiv  Detail & Related papers  (2024-10-24T09:09:20Z)
• Understanding the AI-powered Binary Code Similarity Detection [41.39226562321616]
  AI-powered binary code similarity detection (BinSD) has been widely applied to program analysis. It is difficult to quantitatively understand to what extent the BinSD problem has been solved, especially in realworld applications. We present a systematic evaluation of state-of-the-art AI-powered BinSD approaches.
  arXiv  Detail & Related papers  (2024-10-10T02:13:01Z)
• FoC: Figure out the Cryptographic Functions in Stripped Binaries with LLMs [54.27040631527217]
  We propose a novel framework called FoC to Figure out the Cryptographic functions in stripped binaries. FoC-BinLLM outperforms ChatGPT by 14.61% on the ROUGE-L score. FoC-Sim outperforms the previous best methods with a 52% higher Recall@1.
  arXiv  Detail & Related papers  (2024-03-27T09:45:33Z)
• CEBin: A Cost-Effective Framework for Large-Scale Binary Code Similarity Detection [23.8834126695488]
  Binary code similarity detection (BCSD) is a fundamental technique for various application. We propose a cost-effective BCSD framework, CEBin, which fuses embedding-based and comparison-based approaches.
  arXiv  Detail & Related papers  (2024-02-29T03:02:07Z)
• Combating Bilateral Edge Noise for Robust Link Prediction [56.43882298843564]
  We propose an information-theory-guided principle, Robust Graph Information Bottleneck (RGIB), to extract reliable supervision signals and avoid representation collapse. Two instantiations, RGIB-SSL and RGIB-REP, are explored to leverage the merits of different methodologies. Experiments on six datasets and three GNNs with diverse noisy scenarios verify the effectiveness of our RGIB instantiations.
  arXiv  Detail & Related papers  (2023-11-02T12:47:49Z)
• BiBench: Benchmarking and Analyzing Network Binarization [72.59760752906757]
  Network binarization emerges as one of the most promising compression approaches offering extraordinary computation and memory savings. Common challenges of binarization, such as accuracy degradation and efficiency limitation, suggest that its attributes are not fully understood. We present BiBench, a rigorously designed benchmark with in-depth analysis for network binarization.
  arXiv  Detail & Related papers  (2023-01-26T17:17:16Z)
• UniASM: Binary Code Similarity Detection without Fine-tuning [0.8271859911016718]
  We propose a novel transformer-based binary code embedding model named UniASM to learn representations of the binary functions. In the real-world task of known vulnerability search, UniASM outperforms all the current baselines.
  arXiv  Detail & Related papers  (2022-10-28T14:04:57Z)
• Semantic-aware Binary Code Representation with BERT [27.908093567605484]
  A wide range of binary analysis applications, such as bug discovery, malware analysis and code clone detection, require recovery of contextual meanings on a binary code. Recently, binary analysis techniques based on machine learning have been proposed to automatically reconstruct the code representation of a binary. In this paper, we propose DeepSemantic utilizing BERT in producing the semantic-aware code representation of a binary code.
  arXiv  Detail & Related papers  (2021-06-10T03:31:29Z)
• A Constraint-Based Algorithm for the Structural Learning of Continuous-Time Bayesian Networks [70.88503833248159]
  We propose the first constraint-based algorithm for learning the structure of continuous-time Bayesian networks. We discuss the different statistical tests and the underlying hypotheses used by our proposal to establish conditional independence.
  arXiv  Detail & Related papers  (2020-07-07T07:34:09Z)
• Auto-Encoding Twin-Bottleneck Hashing [141.5378966676885]
  This paper proposes an efficient and adaptive code-driven graph. It is updated by decoding in the context of an auto-encoder. Experiments on benchmarked datasets clearly show the superiority of our framework over the state-of-the-art hashing methods.
  arXiv  Detail & Related papers  (2020-02-27T05:58:12Z)

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.