Integration of Static and Dynamic Code Stylometry Analysis for Programmer De-anonymization

Integration of Static and Dynamic Code Stylometry Analysis for Programmer De-anonymization
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集成静态和动态代码风格分析以实现程序员去匿名化

DOI:
10.1145/3270101.3270110
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发表时间:
2018
期刊:
Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security
影响因子:
--
通讯作者:
Wang, Ting
Wang, Ting
中科院分区:
--
文献类型:
--
作者:
Wang, Ningfei;Ji, Shouling;Wang, Ting

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对匿名代码的作者进行去匿名化(即代码风格测定)会带来重大的隐私和安全影响。大多数现有的代码风格测量方法仅依赖于从源代码中提取的静态(例如,词汇、布局和句法)特征,而忽略了其与常规文本的关键区别——它是可执行的!在本文中,我们提出了 Sundae,一种新颖的代码去匿名化框架,集成了静态和动态风格分析。与现有的解决方案相比,Sundae 有以下显着的不同:(i)它需要更少的静态、手工制作的特征; (ii) 训练所需的标记数据要少得多; (iii) 一旦新程序员的风格测量信息可用,它就可以很容易地扩展到新程序员通过对基准数据集的广泛评估,我们证明了 Sundae 提供了强大的实证性能。例如,在 229 名程序员和 9 个问题的设置下,它在 Python 代码去匿名化方面比 state-of-art 方法高出 45.65%。实证结果强调静态和动态分析的整合是代码风格测量研究的一个有前途的方向。
De-anonymizing the authors of anonymous code (i.e., code stylometry) entails significant privacy and security implications. Most existing code stylometry methods solely rely on static (e.g., lexical, layout, and syntactic) features extracted from source code, while neglecting its key difference from regular text -- it is executable! In this paper, we present Sundae, a novel code de-anonymization framework that integrates both static and dynamic stylometry analysis. Compared with the existing solutions, Sundae departs in significant ways: (i) it requires much less number of static, hand-crafted features; (ii) it requires much less labeled data for training; and (iii) it can be readily extended to new programmers once their stylometry information becomes available Through extensive evaluation on benchmark datasets, we demonstrate that Sundae delivers strong empirical performance. For example, under the setting of 229 programmers and 9 problems, it outperforms the state-of-art method by a margin of 45.65% on Python code de-anonymization. The empirical results highlight the integration of static and dynamic analysis as a promising direction for code stylometry research.
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