  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.
