UniASM: Binary Code Similarity Detection without Fine-tuning

• URL: http://arxiv.org/abs/2211.01144v3
• Date: Thu, 6 Apr 2023 04:49:49 GMT
• Title: UniASM: Binary Code Similarity Detection without Fine-tuning
• Authors: Yeming Gu, Hui Shu and Fan Hu
• Abstract summary: 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.
• Score: 0.8271859911016718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Binary code similarity detection (BCSD) is widely used in various binary analysis tasks such as vulnerability search, malware detection, clone detection, and patch analysis. Recent studies have shown that the learning-based binary code embedding models perform better than the traditional feature-based approaches. In this paper, we propose a novel transformer-based binary code embedding model named UniASM to learn representations of the binary functions. We design two new training tasks to make the spatial distribution of the generated vectors more uniform, which can be used directly in BCSD without any fine-tuning. In addition, we present a new tokenization approach for binary functions, which increases the token's semantic information and mitigates the out-of-vocabulary (OOV) problem. We conduct an in-depth analysis of the factors affecting model performance through ablation experiments and obtain some new and valuable findings. The experimental results show that UniASM outperforms the state-of-the-art (SOTA) approach on the evaluation dataset. The average scores of Recall@1 on cross-compilers, cross-optimization levels, and cross-obfuscations are 0.77, 0.72, and 0.72. Besides, in the real-world task of known vulnerability search, UniASM outperforms all the current baselines.

Related papers

• 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)
• BinSimDB: Benchmark Dataset Construction for Fine-Grained Binary Code Similarity Analysis [6.093226756571566]
  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.
  arXiv  Detail & Related papers  (2024-10-14T05:13:48Z)
• Source Code Foundation Models are Transferable Binary Analysis Knowledge Bases [9.422025563792818]
  Human-Oriented Binary Reverse Engineering aims to lift binary code to human-readable content relevant to source code. We introduce a novel probe-and-recover framework that incorporates a binary-source encoder-decoder model and black-box LLMs for binary analysis.
  arXiv  Detail & Related papers  (2024-05-30T00:17:44Z)
• 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)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• Network Binarization via Contrastive Learning [16.274341164897827]
  We establish a novel contrastive learning framework while training Binary Neural Networks (BNNs) MI is introduced as the metric to measure the information shared between binary and FP activations. Results show that our method can be implemented as a pile-up module on existing state-of-the-art binarization methods.
  arXiv  Detail & Related papers  (2022-07-06T21:04:53Z)
• 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)
• Understanding Self-supervised Learning with Dual Deep Networks [74.92916579635336]
  We propose a novel framework to understand contrastive self-supervised learning (SSL) methods that employ dual pairs of deep ReLU networks. We prove that in each SGD update of SimCLR with various loss functions, the weights at each layer are updated by a emphcovariance operator. To further study what role the covariance operator plays and which features are learned in such a process, we model data generation and augmentation processes through a emphhierarchical latent tree model (HLTM)
  arXiv  Detail & Related papers  (2020-10-01T17:51:49Z)
• Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator [62.26981903551382]
  Variational auto-encoders (VAEs) with binary latent variables provide state-of-the-art performance in terms of precision for document retrieval. We propose a pairwise loss function with discrete latent VAE to reward within-class similarity and between-class dissimilarity for supervised hashing. This new semantic hashing framework achieves superior performance compared to the state-of-the-arts.
  arXiv  Detail & Related papers  (2020-05-21T06:11:33Z)
• One-Shot Object Detection without Fine-Tuning [62.39210447209698]
  We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module. We also propose novel training strategies that effectively improve detection performance. Our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.
  arXiv  Detail & Related papers  (2020-05-08T01:59:23Z)

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.