arXiv cs.AI· Zhiwei Fu, Steven H. H. Ding, Furkan Alaca, Benjamin C. M. Fung, Philippe Charland·· 12 小时前AI 评分18
Pluvio：用迁移学习与条件变分信息瓶颈实现跨未见架构与库的汇编克隆搜索
Pluvio: Assembly Clone Search for Out-of-domain Architectures and Libraries through Transfer Learning and Conditional Variational Information Bottleneck
AI 导读
Pluvio 首次研究面向未见架构与库的汇编克隆搜索问题，通过迁移学习将大规模预训练自然语言模型中的通用知识引入现有学习方法。该系统还用强化学习智能体剔除冗余 token 以缓解序列长度限制，并结合新的变分信息瓶颈学习策略，降低对架构与优化设置潜在指示特征的依赖。在模拟的未见架构克隆搜索场景中，实验结果显示出该方法优于当前最先进方案的有效性。
正文 · 原文
Abstract:The practice of code reuse is crucial in software development for a faster and more efficient development lifecycle. In reality, however, code reuse practices lack proper control, resulting in issues such as vulnerability propagation and intellectual property infringements. Assembly clone search, a critical shift-right defence mechanism, has been effective in identifying vulnerable code resulting from reuse in released executables. Recent studies on assembly clone search demonstrate a trend towards using machine learning-based methods to match assembly code variants produced by different toolchains. However, these methods are limited to what they learn from a small number of toolchain variants used in training, rendering them inapplicable to unseen architectures and their corresponding compilation toolchain variants.
This paper presents the first study on the problem of assembly clone search with unseen architectures and libraries. We propose incorporating human common knowledge through large-scale pre-trained natural language models, in the form of transfer learning, into current learning-based approaches for assembly clone search. Transfer learning can aid in addressing the limitations of the existing approaches, as it can bring in broader knowledge from human experts in assembly code. We further address the sequence limit issue by proposing a reinforcement learning agent to remove unnecessary and redundant tokens. Coupled with a new Variational Information Bottleneck learning strategy, the proposed system minimizes the reliance on potential indicators of architectures and optimization settings, for a better generalization of unseen architectures. We simulate the unseen architecture clone search scenarios and the experimental results show the effectiveness of the proposed approach against the state-of-the-art solutions.
| Comments: | 13 pages and 4 figures. This work has been submitted to the IEEE for possible publication |
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2307.10631 [cs.SE] |
| (or arXiv:2307.10631v2 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2307.10631 arXiv-issued DOI via DataCite |
Submission history
From: Zhiwei Fu [view email]
[v1]
Thu, 20 Jul 2023 06:55:37 UTC (1,468 KB)
[v2]
Fri, 16 Oct 2026 01:20:09 UTC (666 KB)
来源：arXiv cs.AI · arxiv.org