Adversarial attacks against supervised learning algorithms, which necessitates the application of logging while using supervised learning algorithms in software projects. Logging enables practitioners to conduct postmortem analysis, which can be helpful to diagnose any conducted attacks. We conduct an empirical study to identify and characterize log-related coding patterns, i.e., recurring coding patterns that can be leveraged to conduct adversarial attacks and needs to be logged. A list of log-related coding patterns can guide practitioners on what to log while using supervised learning algorithms in software projects.

We apply qualitative analysis on 3,004 Python files used to implement 103 supervised learning-based software projects. We identify a list of 54 log-related coding patterns that map to 6 attacks related to supervised learning algorithms. Using Log Assistant to conductPostmortems forSupervisedLearning (LOPSUL), we quantify the frequency of the identified log-related coding patterns with 278 open source software projects that use supervised learning. We observe log-related coding patterns to appear for 22% of the analyzed files, where training data forensics is the most frequently occurring category.

针对监督学习算法的对抗性攻击，这需要在软件项目中使用监督学习算法时应用日志记录。日志记录使从业者能够进行事后分析，这有助于诊断任何已实施的攻击。我们进行了一项实证研究，以识别和描述与日志相关的编码模式，即可用于进行对抗性攻击并需要记录的重复编码模式。与日志相关的编码模式列表可以指导从业者在软件项目中使用监督学习算法时记录什么。

我们对用于实施 103 个基于监督学习的软件项目的 3,004 个 Python 文件进行定性分析。我们确定了一个包含 54 种与日志相关的编码模式的列表，这些模式映射到与监督学习算法相关的 6 种攻击。使用Log Assistant对Su pervised L earning ( LOPSUL ) 进行事后分析，我们用 278 个使用监督学习的开源软件项目量化了已识别的日志相关编码模式的频率。我们观察到与日志相关的编码模式出现在 22% 的分析文件中，其中训练数据取证是最常出现的类别。