Source Code Authorship Attribution (SCAA) is crucial for software classification because it provides insights into the origin and behavior of software. By accurately identifying the author or group behind a piece of code, experts can better understand the motivations and techniques of developers. In the cybersecurity era, this attribution helps trace the source of malicious software, identify patterns in the code that may indicate specific threat actors or groups, and ultimately enhance threat intelligence and mitigation strategies. This paper presents AuthAttLyzer-V2, a new source code feature extractor for SCAA, focusing on lexical, semantic, syntactic, and N-gram features. Our research explores author identification in C++ by examining 24,000 source code samples from 3,000 authors. Our methodology integrates Random Forest, Gradient Boosting, and XGBoost models, enhanced with SHAP for interpretability. The study demonstrates how ensemble models can effectively discern individual coding styles, offering insights into the unique attributes of code authorship. This approach is pivotal in understanding and interpreting complex patterns in authorship attribution, especially for malware classification.

这篇论文介绍了AuthAttLyzer-V2，它是一种新的源代码特征提取器，用于源代码作者归属的确定，重点关注词法、语义、句法和N-gram特征。论文通过对来自3,000个作者的24,000个源代码样本进行研究，结合Random Forest、Gradient Boosting和XGBoost模型来识别个体编码样式，提供了有关代码作者独特属性的洞察。对于恶意软件分类来说，这种方法对于理解和解释作者归属中的复杂模式至关重要。

AuthAttLyzer-V2: 通过增强集成学习模型揭示代码创作归属性和生成基准数据集